Food Additives & Contaminants by AGROPEC

April 2012

Food Additives & Contaminants Editorial

Brazilian Ministry of Agriculture, Livestock and Food Supply (MAPA): strategies to tackle chemical food safety issues B. Le Bizec

481

Foreword

The National Agricultural Laboratories of Brazil and the control of residues and contaminants in food

Food Additives & Contaminants

Volume 29 Number 4

482

A. de Queiroz Mauricio and E.S. Lins

Articles

Evolution of a residue laboratory network and the management tools for monitoring its performance

490

E.S. Lins, E.S. Conceic° a¬ o and A. De Q. Mauricio

L. Jank, R.B. Ho¡, P.C.Tarouco, F. Barreto andT.M. Pizzolato

Occurrence of antimicrobial residues in Brazilian food animals in 2008 and 2009

C.K.V. Nonaka, A.M.G. Oliveira, C.R. Paiva, M.P. Almeida, C.P. Rezende, C.G.O. Moraes, B.G. Botelho, L.F. Souza and P.G. Dias

In-house validation of PremiÕ Test, a microbiological screening test with solvent extraction, for the detection of antimicrobial

508 517 526

Optimisation and validation of a quantitative and con®rmatory LC-MS method for multi-residue analyses of -lactam and tetracycline antibiotics in bovine muscle C.P. Rezende, M.P. Almeida, R.B. Brito, C.K. Nonaka and M.O. Leite

Determination and con®rmation of chloramphenicol in honey, ®sh and prawns by liquid chromatography±tandem mass spectrometry with minimum sample preparation: validation according to 2002/657/EC Directive F. Barreto, C. Ribeiro, R.B. Ho¡ andT. Dalla Costa

Simultaneous determination of chloramphenicol and ¯orfenicol in liquid milk, milk powder and bovine muscle by LC±MS/MS D.R. Rezende, N. Fleury Filho and G.L. Rocha

Producing a sulfamethazine quality control material under the framework of ISO/CD Guide 80

541

ISSN 1944–0049

Food Additives & Contaminants . . .

PART A: CHEMISTRY

ANALYSIS

CONTROL

EXPOSURE & RISK ASSESSMENT

Ministério da Agricultura, Pecuária e Abastecimento do Brasil – Laboratórios Nacionais Agropecuários: Methods of analysis for residue and contaminants in the food chain

550 559 571

A.L. Cunha, P.F. Silva, E.A. Souza, J.R.A.M. Ju¨ nior, F.A. Santos and E.A.Vargas

Bioactivity-based screening methods for antibiotics residues: a comparative study of commercial and in-house developed kits R. Ho¡, F. Ribarcki, I. Zancanaro, L. Castellano, C. Spier, F. Barreto and S.H. Fonseca

Optimisation and validation of a quantitative and con®rmatory method for residues of macrolide antibiotics and lincomycin in kidney by liquid chromatography coupled to mass spectrometry C.P. Rezende, L.F. Souza, M.P. Almeida, P.G. Dias, M.H. Diniz and J.C. Garcia

Validation of a rapid and sensitive routine method for determination of chloramphenicol in honey by LC±MS/MS T.Taka, M.C. Baras and Z.F. Chaudhry Bet

(

TFAC-29-4.indd 1

535

April 2012

residues in poultry muscles

C.G. Magalha¬ es, C.R. De Paiva, B.G. Botelho, A.M.G. De Oliveira, L.F. De Souza, C.V. Nonaka, K.V. Santos, L.M. Farias and M.A.R. Carvalho

Number 4

Validation of a quantitative and con®rmatory method for residue analysis of aminoglycoside antibiotics in poultry, bovine, equine and swine kidney through liquid chromatography-tandem mass spectrometry

497

Volume 29

-lactam antibiotics residues analysis in bovine milk by LC-ESI-MS/MS: a simple and fast liquid±liquid extraction method

Volume 29 Number 4

577 587 596

Continued on inside back cover)

3/15/2012 6:53:51 PM

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 481

EDITORIAL Brazilian Ministry of Agriculture, Livestock and Food Supply (MAPA): strategies to tackle chemical food safety issues This special issue of Food Additives and Contaminants is dedicated to Brazil and to the strategies implemented by the Ministry of Agriculture, Livestock and Food Supply (MAPA), to tackle chemical food safety issues. This issue features a selection of papers arising mainly from work conducted within laboratories belonging to MAPA. The papers deal with the determination of chemicals, such as heavy metals, polycyclic aromatic hydrocarbons, phytosanitary products, mycotoxins, veterinary drugs or dyes that are introduced into foods either as a result of their occurrence in the environment, natural infection by fungi, or other human activities. The agricultural sector in Brazil exhibits an impressive number of agricultural establishments responding directly to the favourable balance of the national trade results (a positive balance of US$60 billion in 2009). Facilitation of the trade implies a mandatory compliance with international rules. In particular, papers presented in this special issue focus on the European regulations involving compounds/ matrices of interest, maximum residue limits, criteria for sampling, performance and validation criteria for analytical methods as well as quality assurance of the results issued by the laboratories. This context has led to the development of rapid screening methods for

ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2012.662768 http://www.tandfonline.com

various analytes based on immunochemical techniques. Furthermore, highly sophisticated multianalyte methods based on liquid chromatography coupled with high-resolution and/or multipledimension mass spectrometry have been implemented to allow identification and simultaneous determination of a wide range of residues/contaminants. Thanks to the contributors, to whom we would like to express our great gratitude, 24 papers have been published in this special issue, which evidences a real attempt to work up Brazilian food control to the highest international standards. I am also very thankful to the Editorin-Chief of the journal, John Gilbert, and Managing Editor, Victoria Gardner, for their kind support, to the skilled scientists who have been involved in the assessment of the papers, and to Dr. Gaud DervillyPinel for her help in the management of the reviewing process. Bruno Le Bizec, Prof, Dr, HDR Guest Editor LABERCA, ONIRIS Nantes, France Email: bruno.lebizec@oniris-nantes.fr

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 482–489

FOREWORD The National Agricultural Laboratories of Brazil and the control of residues and contaminants in food A. de Queiroz Mauricio* and E.S. Lins Ministry of Agriculture, Livestock and Food Supply of Brazil, Esplanada dos Ministe´rios, 70043-900 Brası´lia-DF, Brazil (Received 26 November 2010; final version received 13 July 2011) The laboratory activity of the Ministry of Agriculture, Livestock and Food Supply in Brazil has a history that is richer than most people are aware of. The institutions that today are known as National Agricultural Laboratory – Lanagros – were once a smaller initiative that suffered ups and downs throughout the decades. The recognition that the Lanagros have today – as reference centres with open communication channels with some of the world’s greater laboratories in residue and contaminants in food analyses – is the fruit of several years of hard work, good ideas and a strong will never to let down society. Today the Lanagros act not only by performing analyses for the monitoring and investigation programmes, but also in the research and development of analytical methods, providing technical advice on the elaboration of guidelines and normatives, international negotiation and the evaluation of other laboratories. The Lanagros work in an ISO 17025 environment, and they are now being directed and prepared to be able to respond to outbreaks and crises related to the presence of residues and contaminants in food, with the readiness, quickness and reliability that an emergency requires. Investments are allocated strategically and have been giving concrete results, all to the benefit of consumers. Keywords: animal; vegetables; fruit; cereals; residues; veterinary environmental contaminants; regulations; quality assurance

The early days initiative Moving back in a timeline towards the origins of the laboratory system of MAPA, one can identify in the early days disease diagnostics, the quality control of raw materials and both animal and plant products laboratories, which were mostly located on the farms of the Ministry of Agriculture and where part of government official controls took place. The Ministry of Agriculture was created in 1860 – at that time it was named the Ministry of Agriculture, Industry, Commerce and Public Works. At the very beginning of the twentieth century, and within the wide range of responsibilities of the Ministry of Agriculture, some facilities were created in close relation with laboratory activities, such as the Superior School of Agriculture and Veterinary Medicine, Agricultural Inspection and Defense Service, Geological and Minerological Service, and the Chemistry Institute (Santos 2006). This was an important step towards improved science education in Brazil, especially the establishment of chemistry courses linked to the Ministry’s activities, as the Ministry of Education and Health was only subsequently created in 1930. In those days the Ministry of Agriculture contained several departments of applied chemistry throughout the country: Laboratories of Agricultural and Food

residues;

pesticide

residues;

Chemistry, of special studies on rubber in the Amazon region, of sugar analysis, laboratories attached to the Fisheries Stations, to the National Museum and the Botanical Garden. In parallel, municipalities were also establishing laboratories for foodstuff control. However, for reasons that would require further investigation, these remarkable facilities were closed one after the other (Santos 2006). In 1918, the Laboratory of Defense Inspection of butter, a working station responsible for the analysis of dairy products consumed in Brazil, was transformed into the Chemistry Institute. Among the responsibilities of this new institute, the legal mandate also included:

*Corresponding author. Email: angelo.mauricio@agricultura.gov.br This paper is kindly dedicated to the one I love. ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.620987 http://www.tandfonline.com

drug

. Research in specialties of agriculture, industry and livestock. . Tests and chemical studies for commercial, private, state and municipality government purposes. . Teaching of chemistry aimed at capacitybuilding for technicians. . Studies on fodder from a scientific perspective. . Inspection of butter and dairy products. . Inspection of fertilisers, insecticides and fungicides.

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Table 1. Year of foundation of the laboratories that would later be the Lanagros. Old name LARA Pedro Leopoldo, MG LARA Campinas, SP LARA Porto Alegre, RS Labortatory of Animal Diseases and anti-rabies Vaccines, PE LARA Bele´m, PA Laboratory of Vaccines for Foot and Mouth Disease and Rabies

A Presidential Decree from the same year, 1918, stated that the Chemistry Institute would also be responsible for establishing assay methods for the food laboratories (Faria et al. 2010). What is also remarkable is that the Chemistry Institute courses are considered the first major official chemistry courses available in Brazil, but they were active only until 1926. One can see an important initiative from the bureaucrats of those days in establishing an analytical capability within the ministry, with a practical focus on innovation and development. Sadly, the courses have changed. Nevertheless, the ministry eventually realised that the inspection actions would not be carried out properly without laboratory scrutiny. Focused on inspection and defence demands, and because of a need for laboratory support linked to those actions, the Ministry of Agriculture created a number of official laboratories, which would later become the National Agricultural Laboratories – Lanagros. In 1976, a Presidential Decree created a Reference Laboratory Network; and in 1978 a Ministerial Ordinance transformed the existing structures into Reference Laboratories, each responsible for covering a region of Brazil (BRASIL 1978). The Regional Laboratories of Animal Reference (LARA) were established at different times (Table 1). They later merged with the Regional Laboratories of Plant Reference (LARV) to become one entity.

The agricultural laboratory network milestones Over more than 30 years the activity of these laboratories was considered a support of minor importance to the main agricultural inspection operation, which was regarded as an activity isolated from those inspection activities. However, along with an increase in the country’s production and retail volumes, as well as the constant upgrades observed in electronics, computers, nanotechnology, pharmacy, genetics, agricultural practices and, above all, analytical and instrumental chemistry, a clear move can be noticed towards the detection and identification of substances at a frequency and at concentrations unthinkable in the early days.

Year of foundation

Actual name

1983 1979 1950s 1947 1949 1948

Lanagro-MG Lanagro-SP Lanagro-RS Lanagro-PE Lanagro-PA Lanagro-GO

The significant worldwide expansion in food demand and commercialisation has posed new challenges to governments, precipitated by the current large scale of food production. On a global perspective, world trade increased by 17.6% in 2008 at US$12.6 trillion, US$853.2 billion of it solely related to food and agricultural products. In this scenario, the Brazilian export of such commodities began to expand in the worldwide market from 2003 onwards (BRASIL 2010). In 2002 Brazil had 4.6% of the agricultural world market; since then this figure has increased by 2.2 percentage points, achieving 6.8% of the total global market at present (BRASIL 2010). This increase is related to the fact that special efforts were directed towards the improvement of Brazil’s capacity regarding the production and export of goods. As an example, in 2009 Brazil exported US$54.8 billion in products originating from its agribusiness, the second highest value since 1997 (BRASIL 2010). Considering Brazil’s export basket, the participation of agricultural products in total Brazilian exports shifted from 23.9% in 2000 to 35.8% in 2009. This increase is directly related to the quantities of goods such as food produced and exported from 2000 to 2009, despite the oscillations in prices observed in the same period due to global crises. The amount of Brazilian agricultural products exported increased by 164.8% between 2000 and 2009 (BRASIL 2010), which explains the expansion of Brazilian participation in the global market. In a more detailed and particular analysis, the expansion of the quantities exported is linked to the increase of the exportable surplus which may be associated with the difference between the rate of population and production growth. As an example, while grain production in Brazil increased by 77.0% over the last decade, population growth was only 13.7%, which explains the generation of a surplus. The five main contributing areas to this scenario were soya complex, meat and meat products, sugar and alcohol complex, coffee and tobacco, jointly responsible for US$46.1 billion of the total US$54.8 billion exported, i.e. 84.0% of total Brazilian exports (BRASIL 2010). Together with recent improvements in technology, this remarkable increase in the volumes of food production and consumption demanded exceptional performance

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A. de Queiroz Mauricio and E.S. Lins

Figure 1. Geographical distribution of the six Lanagros and their SLAV – Advanced Laboratory Units.

from the laboratories and principally required that they were completely capable of keeping up with this evolution, at the same time as regulations related to food safety were being tightened by governments all over the world. It also required a new integrated approach to governmental control, in which the laboratory activities were an inseparable part of further inspection operations, the laboratories acting directly on the verification and validation of the production and processing systems of agriculture by giving analytical insights, rather than by simply providing test reports. These pressures led the Brazilian government recently to carry out a reorganisation of MAPA’s laboratory system, with the objective of updating and improving the policies and analytical activities related to plant and animal defence and health. Thus, a national laboratory network was established, with the responsibility of performing studies and assays in order to assess the conformity of the agricultural inputs and food produced domestically, especially concerning official control of residues and contaminants. Nowadays that network comprises the six National Agricultural Laboratories – formally created in 2005 by Presidential Decree No 5.351 and designated in

2006 as the official laboratories by Presidential Decree No 5741 (BRASIL 2005, 2006), which are the official laboratories of the Ministry of Agriculture, Livestock and Food Supply, with the responsibility of serving as the national reference in laboratory activities regarding agricultural health and defence as legal mandate. Today the legal mandate of the Lanagros includes activities such as: . Official analyses. . Assays for inspection, monitoring and other legal purposes. . Laboratory audits. . Research and development of analytical methods. . Elaboration and review of legislation and technical guidelines. . Participation in international negotiations. The six Lanagros are located in the five geographical regions of Brazil (with two in the southeast region, the country’s main industrial and production site), and they therefore act as centres of production and diffusion of analytical knowledge and laboratory policies in each of those regions as depicted in Figure 1. The Lanagros were conceived as multidisciplinary centres and parts of

Lanagro 1

3 1 0 2 0 0 4 0

1 1

0 0 0 1

Pharmacists Veterinarians

Agronomists

Biologists/engineers

Other areas

Undergrad/support Chemists

Pharmacists

Veterinarians

Agronomists

Biologists/engineers Other areas Undergrad/support Chemists

Number of permanent technicians

Chemists

Areas of expertise

Staff

0 0 0 1

4 0

1 0

Number of technicians under contract

Table 2. Summary of the analytical capability of Lanagros.

Number of associate researchers Main equipments in the operation

(continued )

AAS equipped with background correction capability and data-handling system, PerkinElmer Model AAnalyst 100; FIAS Flow injection analysis system working in the metal hydride mode, PerkinElmer Model FIAS 400; autosampler PerkinElmer Model AS-90 GTA graphite furnace atomic absorption equipped with Zeeman background correction Varian Model 240Z; autosample Varian Model 120; Quick Trace M-6100 Mercury Analyzer equipped with peristaltic pump; ASX-400 autosampler, system working in the metal hydride mode Microwave application for acid digestion, equipped with ramp to temperature as 1600 W, CEM, model MARS Xpress Varian HPLC System equipped with: ProStar 410TM HPLC AutoSampler with cooling option and standard sample tray: 84 1.5 ml vials with 3 10 ml vials; ProStar 363 fluorescence detector, with continuous Xenon lamp, wavelength range 200–731 nm for excitation and 200–900 nm for emission; ProStar 335TM Diode Array Detector, with wavelength range 190–950 nm, light source: D2 and quartz halogen; ProStar 240TM HPLC solvent delivery modules, ternary gradient pump; ProStar 210 HPLC solvent delivery modules, isocratic pump; ProStar 500TM column valve module, can accommodate up to six analytical or two semi-prep columns and two individually controlled heaters; control and data handling GalaxieTM Chromatography Data System, Galaxie Workstation

LC-MS/MS system, Applied Biosystems, API 5000, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI; LC-MS/MS system, Applied Biosystems, API 5000, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI; LC-MS/MS system, Waters, Quattro Micro, mass analyser triple quadrupole with ionisation source ESI/APCI HPLC system, Shimadzu, with fluorescence, UV-VIS and diode array detectors GC system, Thermo, Trace, with electron capture detector (ECD); hydride generation atomic absorption spectrometry (CV AAS), Analisty 200 with FIAS/100 (PerkinElmer, USA) Cold vapour atomic absorption spectrometry (CV AAS), Model FIMSÕ 400 Mercury Analysis System with AS91 Autosampler (PerkinElmer, USA) Graphite furnace atomic absorption spectrometry (GF AAS), Analisty 600 with AS 800 Autosampler (PerkinElmer, USA) Graphite furnace atomic absorption spectrometry (GF AAS), Model 4110ZL with AS 72 Autosampler (PerkinElmer, USA)

Analytical capabilities

Food Additives and Contaminants 485

Lanagro

Staff

5 1 0 0 0 2

Pharmacists

Veterinarians

Agronomists

Biologists/engineers

Other areas

Undergrad/support

0 0 0 0 0 4

0 0 4 0

Biologists/engineers Other areas Undergrad/support Chemists

Veterinarians Agronomists Biologists/engineers Other areas Undergrad/support Chemists

Agronomists

2 1

Veterinarians

Chemists Pharmacists

Number of permanent technicians

Pharmacists

Areas of expertise

Table 2. Continued.

1 0 0 0 2 0

0 0

0 0 0 3

Number of technicians under contract

Number of associate researchers Main equipments in the operation

LC-MS/MS System, Applied Biosystems, API 5500, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI00 ; GFAA System, PerkinElmer, AA800; mercury analyser, Milestone, DMA-80 Mercury analyser (CV-AAS), CETAC, M-6100; HPLC-UV-fluorescence, Shimadzu Microwave, Anton-Paar, MV3000; microwave, Milestone, ultraclave UV-VIS, Varian, Cary 50 Conc; high shear mixer, Silverson, DX 60-2 unt Mill, Retsch, SK100

GF AAS and HG AAS systems, PerkinElmer, AAnalyst 800, atomic absorption spectrometer with graphite furnace and hydride generation techniques; GF AAS system, PerkinElmer, AAnalyst 600, atomic absorption spectrometer with graphite furnace technique SS TDA AAS system, Milestone, DMA-80, solid sampling thermal decomposition amalgamation atomic absorption spectrometer GC/MS-Shimadzu QP-2010, gas chromatograph with mass spectrometer detector; GC/ECD Thermo Finnigan, gas chromatograph with electron-capture detector GC-HRMS system with Kit autosampler model Al 3000, Mat 95XP, Thermo Finnigan, highresolution mass analyser LC-MS/MS system, Applied Biosystems, API 5000, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI00 LC-MS/MS system, Thermo Finnigan, Quantum Ultra, mass analyser triple quadrupole with ionisation source ESI LC-MS/MS system, Thermo Finnigan, TSQ Quantum Access, mass analyser triple quadrupole with ionisation source ESI

LC-MS/MS system, Applied Biosystems, API 5000, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI LC-MS/MS system, Waters, Quattro Premier, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI GC-MS system, Thermo Finnigan, mass analyser single quadrupole with ionisation source EI/CI Mercury analyser, Quick Trace M6100, atomic absorption spectrometer, equipped with autosampler ASX-400 Atomic absorption-graphite furnace-hydride generation â&#x20AC;&#x201C; PerkinElmer-A600-FIAS100 Microwave sample preparation, ANTON PAAR, Multiwave 3000

Analytical capabilities

486 A. de Queiroz Mauricio and E.S. Lins

Pharmacists

Veterinarians

Agronomists

Biologists/engineers

Other areas

Undergrad/support

HRGC-MS/MS system, Agilent, 7000A model, mass analyser triple quadrupole with electronic impact and chemical ionisation sources HRGC/EI/CI; HRMS-HRGC system, high-resolution mass spectrometer system, Waters, AutoSpec Premier model, magnetic sector analyser with electronic impact, coupled with gas chromatograph Agilent, 6890N model with PTV injector, HRMS/HRGC/EI HRGC-MS system, Thermo Corporation, DSQ model coupled with gas chromatograph Thermo/Trace GC Ultra model, HRGC/MS/EI; HRGC-MS/MS system, Thermo Corporation, Polaris Q model coupled with gas chromatograph Thermo/Focus GC model, impact electronic and chemical ionisation sources, HRGC-MS/MS/EI/CI (ion trap); HRGC-MS/MS system, Thermo Corporation, Polaris Q model coupled with gas chromatograph Thermo/Focus GC model, impact electronic and chemical ionisation sources, HRGC-MS/MS/EI/CI (ion trap); DMA, Milestone, DMA-80, direct mercury analyser Gas chromatograph Thermo/Trace GC Ultra model with ECD and FID detectors, HRGC/ ECD/FID; LC-MS/MS system, Applied Biosystems, API 5500, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI; LC-MS/MS system, Waters, quadrupole time of flight (G1) – TOF; FIAS, PerkinElmer, FIAS-400, flow injection analysis system; microwave digestion system, Anton-Paar, Multiwave 3000; acid purification system, Milestone, DuoPUR ICP-MS, Varian, 820-MS, inductively coupled plasma mass spectroscopy; GF AAS, PerkinElmer, AA-600, graphite furnace atomic absorption spectrometry; FAAS, PerkinElmer, AA-400, flame atomic absorption spectrometry; FAAS, PerkinElmer, AA-100, flame atomic absorption spectrometry; Milli-Q Advantage, Millipore, A10 Element, water purification system unit; toxic organic sampler used for sampling of dioxins, PCBs, pesticides and PAHs in air. Amotox – Energe´tica Air Quality RRLC Agilent - MS/MS system, Applied Biosystems, API 5000, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI; RRLC Agilent - MS/MS system, Applied Biosystems, API 5000, mass analyser triple quadrupole with ionisation source ESI/ APCI/APPI; UFLC Shimadzu-MS/MS system, Applied Biosystems, API 4000 QTrap, mass analyser triple quadrupole with ionisation source ESI/APCI/APPI; UPLC AcquityMS/MS system, Waters, Quattro Premier XE, mass analyser triple quadrupole; HPLC-MS/ MS system, Waters, Quattro Premier XE,mass analyser triple quadrupole HPLC – Shimadzu, SCL-10VP, with fluorescence and UV detector and positive column derivatisation using Kobra cell; HPLC – Shimadzu, SCL – 10Avp, with fluorescence detector and iodine positive column derivatisation; HPLC – Shimadzu, SIL-HTC Prominence, with fluorescence detector and positive column derivatisation using Kobra cell; HPLC – Shimadzu, SBM – 20A Prominence, with fluorescence, UV and DAD detector and positive column derivatisation system HPLC, Marca: Shimadzu, RP 006.212, RI Lacqsa 511; LC-MS/MS system, Varian 1200L, Quadrupole; GC-MS/MS system, Agilent Technologies, 7890A model, mass analyser triple quadrupole with electronic impact and chemical ionisation sources HRGC/EI/CI; 03 Automated sample processor system (ASPECXl); Espectrophotometer, Shimadzu, UV-1601 PC

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an unified system for the attention of agricultural health, which is coordinated by a central instance in Brası´ lia that brings various agents together involved in concerted actions. In direct communication with the other inspection bodies of MAPA and states, they act by spreading analytical capabilities in the areas where inspections are present, providing the required laboratorial services with agility and efficacy. Additionally, a research, development and innovation branch was introduced in the Lanagros with the objective of conducting exploratory studies and in anticipation of emerging food-borne risks in a proactive approach focused on the speed of response and emergency preparedness. This scenario demands that the Lanagros also expand their scopes. Therefore, there are several new methods, especially multi-residues, being developed and implemented in the analytical routine, and counting on the expertise of both old and new staff who develop research activities specifically on this matter in their workplans.

Today Today the Lanagros act in the following areas in the spectrum of action of the Ministry of Agriculture: . Residues and contaminants in food. . Agrochemicals including pesticides. . Physical chemistry of products of animal origin and water. . Physical chemistry classification of plant products. . Physical chemistry of feed. . Physical chemistry of beverages and vinegar. . Animal diagnoses. . Plant diagnoses. . Physical chemistry of fertilisers, soil corrective, substrates and correlates. . Inoculants and analogous compounds. . Microbiology of food and water. . Biotechnology and genetically modified organisms (GMOs). . Milk quality. . Genetic identification and animal semen. . Control of veterinary products. . Seeds and seedlings. The residues and contaminants area, which is a strategic front of the Lanagro Network, and on which the Agricultural Defense Secretariat relies substantially, is responsible for the execution of the Brazilian National Residue and Contaminants Control Plan (PNCRC). In this area the scope of the Lanagros is divided into various fronts: veterinary drug residues; organic contaminants and inorganic contaminants in products of animal origin; mycotoxins; and pesticide

residues in products of plant origin. The PNCRC comprises almost 20,000 samples collected throughout the year from producers in every state of the country. The Lanagros has state-of-the-art analytical chemistry equipment, with techniques based mainly on liquid and gas chromatography coupled with mass spectrometry (triple quadrupole and time-of-flight), and gas chromatography coupled with high-resolution mass spectrometry, atomic absorption spectroscopy, and inductively coupled plasma spectroscopy, besides microbiological screening methods. The laboratory staff are constantly seeking development and innovation in terms of analytical methodology, aiming to optimise the time of analysis, capacity, resources and quality. All the Lanagros’ analysis methods in the residues and contaminants areas follow rigorous validation and internal quality control so that the issued results are substantiated. An executive summary of the analytical capability of the Lanagros including the main equipment and staff available is shown in Table 2. There has been also a great effort in opening technical cooperation with reference centres around the world, as a valuable experience for the interchange of advances in techniques allied to the developments and innovation focused on continuous improvement and faster response to emerging risks in food production and consumption. Examples of these cooperations currently in place are projects such as ‘Project UE/Mercosur/SPS ALA/2005/17887’ aimed at cooperation for the harmonisation of veterinary, phytosanitary, and food safety standards and procedures; and ‘Project UE/Brasil ALA/2004/006-189’ aimed at the internationalisation of Brazilian enterprises, as well as bilateral cooperation for capacity-building with international reference centres such as CFIA in Canada, Wageningen-UR and RIKILT in the Netherlands (LNV-BOCI Project 2010), SARAF/ LABERCA in France, and the Japan International Cooperation Agency (JICA). Research initiatives such as the ‘MycoRed Project – IFA-Tulln Center for Analytical Chemistry, Austria’ and ‘Aflatoxins in Brazil Nuts and their Shells, FSA call RRD 31 – FERA, UK’ are also being carried out with the active participation of the Lanagros. In a concerted action by both MAPA and the Ministry of Science and Technology of Brazil (MCT), a project was launched for the allocation of specialists through scholarships for MSc and PhD degrees, directed towards the improvement of the residues and contaminants area at the Lanagros. The project is being conducted together with the The National Council for Scientific and Technological Development (CNPq), which is a Federal Agency linked to the MCT dedicated to the promotion of scientific and technological research and to capacitybuilding of human resources for research in the country (its history is directly linked to the scientific

Food Additives and Contaminants and technological development of Brazil). According to the rules of the CNPq, these scholarships are classified as ‘Scholarships for Technological Development and Innovative Extension’ which are destined by the CNPq for the use of specialists and scientists focused on: (1) the execution of applied research projects; (2) the execution of technological development projects; and (3) activities of innovative extension and transfer of technology. The specialists selected are mainly responsible for activities such as the development and validation of analytical methods for substances/matrices currently lacking; the implementation and optimisation of instrumental laboratories; the transfer of knowledge and technology to Lanagros’ staff; the solution of QA/QC issues focusing on accreditation; the production of reference materials; undertaking engineering and architecture technical projects; the development and use of statistical tools applied to laboratory work; and improvements to the procedures related to the management of residue and contaminants laboratories. Since 2008, 109 scholarships for MSc and PhD degrees were made available for the residue and contaminants areas at the Lanagros, drawing together national and international experts and scientists from areas such as analytical chemistry, engineering, pharmacology, veterinary and agronomy sciences, mathematics and statistics, biology, quality assurance, architecture, administration, etc. The ultimate purpose of this project is to establish the Lanagros as high-level science-oriented institutions able to produce advanced technological solutions and respected by the scientific community and trusted by society. Lanagro Network’s Vision is to be recognised as a world reference point in agriculture and livestock laboratory services, capable of providing quick responses of high quality and scientific excellence, and striving for innovation, rigour and efficiency in the delivery. For this, investments were prioritised into three main areas: capacity-building, infrastructure and quality management tools. In 2009/2010, a total budget of more than US$5.5 million was allocated to different projects according to strategic needs aimed at

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actions divided into these three main areas (including routine costs). Particularly in the case of infrastructure, the budget was directed to diagnostic studies and surveys, as well as to minor refurbishments and the funding of technical engineering and architecture projects (resources for buildings and major refurbishments need to be foreseen for the next biennium). The Lanagros recognise that it is necessary to be able to respond quickly and efficiently to outbreaks and crises. Significant effort and resources have been directed to make the Lanagros able to foresee public health concerns, working together with other departments in MAPA, not only to protect Brazilian consumers, but also to continue developing trade. It is time to be one step ahead. As in any timeline, ‘This is not the end. It’s not even the end’s beginning. But it might be the beginning’s end . . . .’

References BRASIL, Ministe´rio da Agricultura. 1978. Portaria Ministerial no. 904, de 29 de Setembro de 1978, Dia´rio Oficial da Uniaõ. Brası´ lia (Brazil). BRASIL, Ministe´rio da Agricultura, Pecua´ria e Abastecimento. 2005. Decreto Presidencial no. 5351, de 21 de janeiro de 2005, Dia´rio Oficial da Uniaõ. Brası´ lia (Brazil). BRASIL, Ministe´rio da Agricultura, Pecua´ria e Abastecimento. 2006. Decreto Presidencial no. 5741, de 30 de março de 2006, Dia´rio Oficial da Uniaõ. Brası´ lia (Brazil). BRASIL, Ministe´rio da Agricultura, Pecua´ria e Abastecimento. 2010. Intercaˆmbio Comercial do Agronegoćio: principais mercados de destino. Brası´ lia (Brazil). Faria LR, Caˆmara BP, Fonseca MR. 2010. Instituto de Quı´ mica. In: Diciona´rio Histo´rico-Biogra´fico das Cieˆncias da Sau´de no Brasil (1832–1930); [cited 2010 Nov 25]. Available from: http://www.dichistoriasaude. coc.fiocruz.br/ Santos NP. 2006. Façamos Quı´ micos – a ‘certidaõ de nascimento’ dos cursos de quı´ mica de nı´ vel superior no Brasil. Quı´ m Nova. 29(3):621–626.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 490–496

Evolution of a residue laboratory network and the management tools for monitoring its performance E.S. Lins*, E.S. Conceic¸a˜o and A. De Q. Mauricio Ministry of Agriculture, Livestock and Food Supply of Brazil, Esplanada dos Ministe´rios, Annex B, Room 433 – Zip code: 70043-900, Brası´lia-DF, Brazil (Received 26 November 2010; final version received 16 December 2011) Since 2005 the National Residue & Contaminants Control Plan (NRCCP) in Brazil has been considerably enhanced, increasing the number of samples, substances and species monitored, and also the analytical detection capability. The Brazilian laboratory network was forced to improve its quality standards in order to comply with the NRCP’s own evolution. Many aspects such as the limits of quantification (LOQs), the quality management systems within the laboratories and appropriate method validation are in continuous improvement, generating new scenarios and demands. Thus, efficient management mechanisms for monitoring network performance and its adherence to the established goals and guidelines are required. Performance indicators associated to computerised information systems arise as a powerful tool to monitor the laboratories’ activity, making use of different parameters to describe this activity on a day-to-day basis. One of these parameters is related to turnaround times, and this factor is highly affected by the way each laboratory organises its management system, as well as the regulatory requirements. In this paper a global view is presented of the turnaround times related to the type of analysis, laboratory, number of samples per year, type of matrix, country region and period of the year, all these data being collected from a computerised system called SISRES. This information gives a solid background to management measures aiming at the improvement of the service offered by the laboratory network. Keywords: regulations; AAS; chromatography – GC/MS; chromatography – LC/MS; heavy metals; mycotoxins; pesticide residues; veterinary drug residues; animal products – meat; vegetables; fish and fish products

Introduction A major part of the Brazilian National Residue & Contaminants Control Plan (NRCCP) is related to laboratory assays. In order to verify and monitor consumer exposure to residues and contaminants, samples are collected by official inspectors according to an annual sampling plan and sent for analyses in a laboratory network, coordinated by the Ministry of Agriculture (BRASIL 2008). Figures 1a and 1b show the size of the Brazilian NRCCP in terms of analytical programme, and the distribution among the laboratories that are part of the network, here referred as LANAGROs (official laboratories) and authorised laboratories (both private and public laboratories), in 2007 and 2008 respectively (Mauricio et al. 2009). Region Southeast holds the largest number of laboratories because it has concentrated in it a large number of the industrial establishments in the food chain (especially for swine and poultry) when compared with the other regions; and consumers; as well as

*Corresponding author. Email: erick.lins@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.653988 http://www.tandfonline.com

high industrial levels for laboratorial resources and inputs. In order to cover better areas in the Brazilian territories, the Ministry of Agriculture has taken two important initiatives, which are: the provision of a second network composed by laboratories from universities and governmental research centres intended to merge within the actual network when technicaly prepared; and the development of collaborative centres focused on residues and contaminants. Both projects are held in close cooperation between the Ministry of Agriculture and the Ministry of Science and Technology, and the centres receive resources and financial support with the clear objective to develop and enhance the analysis of residues and contaminants in food. A correlation can be observed in Figures 2b, 3a and 3b, since the major part of the samples originate from the South and Southeast regions in which are located most of the establishments for swine and poultry,

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Figure 1. Number of samples destined for each laboratory participating on the National Plan.

Figure 2. (A) Geographical distribution of the laboratories. (B) Number of samples per region.

which in turn represent more than 50% of the NRCP samples. The sampling programme is based on Codex Alimentarius guidelines, and is designed, monitored and statistically reviewed every year by the Coordination of Control of Residue and Contaminants within the Ministry of Agriculture. It can be seen from Figure 4 that the distribution of samples throughout the year was not uniform, with periods of overload of operational capacity of the laboratories, especially by the end of the year and the end of the plan. However, in order to avoid an overflow of samples coming into the laboratories in a concentrated period, the managers at the central level strengthened the controls of the weekly â&#x20AC;&#x2DC;draw and collectâ&#x20AC;&#x2122; of samples by verifying the number of samples actually tested, the number of rejections, and the number of samples needed for the completion of the programme. In this way the routine was constantly assessed and eventual problems in the network capability were quickly identified and

alternate routes taken to keep up with the NRCP schedule.

Monitoring laboratory performance Between 2007 and 2009, some measures were taken to discipline the laboratoriesâ&#x20AC;&#x2122; procedures specifically for NRCCP samples. One of those measures was the Procedural Manual for Laboratories, issued at the beginning of 2008, and which dictates exactly how a laboratory should routinely behave. The present work shows the efficacy of these measures, reflected in the improvement of the whole laboratory network performance. The performance of the laboratories was assessed with regards to their turnaround time, accreditation status and performance in PT programmes. Taking into account that the scopes hold reasonable differences, some variables were considered, such as the number of samples received yearly, the number of

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Figure 3. (A) Number of samples taken for each species. (B) Profile of the distribution of species sampled in each region.

Figure 4. Seasonal distribution of the amount of samples.

Food Additives and Contaminants methods running in the laboratory, and the availability of PT providers. All these data were gathered and assessed with a computerised system called SISRES. The system comprises the steps of the National Plan, from the selection of the sampling site, species, matrices and analytes to test for, to the reception of the samples by the laboratories and the report of results into a database. It links the inspectors in the field, the laboratories and managers at the headquarters, all with different privileges of access to specific information on their user profiles. The database allows the managers of the NRCCP to verify the number of samples collected, the number of samples discarded for different reasons, the turnaround times of the laboratories and the results of the analyses. A computerised system for the laboratory information management is under development; it will hold data from all laboratories integrated in the network, giving the network manager tools to assess laboratories performance in real time. Not only that, this system is being designed also to keep complete validation data for each laboratory. The turnaround time (TRT) comprises procedures for sample reception, the analysis per se, the update of SISRES with the launch of results and report of results on the certificate of analysis. Following the seasonal sample distribution, the turnaround times may vary proportionaly. It can be noted that a large concentration of samples arrived in the laboratories by the end of 2007, taking them more time for analysis. This tendency was observed in 2008, but to a lesser extent.

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Taking into account all kinds of analyses of NRCCP, it was found that the global average decreased from 17.6 in 2007 to 12.3 in 2008. At the beginning of 2008, CGAL took some measures in order to coordinate better the laboratory network and thus improve the quality of the service provided to the inspection body. These measures comprised the issuing of a Procedural Manual containing guidelines from the reception, through the analysis and to the report of results, followed by a policy of more assessment on their compliance to normatives and deadlines observed by them. This led to a considerable improvement in reliability of results as well as generally lower turnaround times, taking into account different kinds of analysis performed in different laboratories. Figure 5 depicts the turnaround times per laboratory distributed in percentiles; Table 1 shows the mean turnaround times of those laboratories. One can observe a considerable improvement in the number of analyses carried out within the turnaround time recommended, i.e., 15 working days. The aim was to achieve 100% of analyses conducted in this period for the whole laboratory network, and mostly they were close to that goal, with the solely exception of one laboratory which is not part of the network since 2008, and one of the Lanagros, which in this case received a large number of samples originally destined for other laboratories (data were not considered in this case). Authorised laboratories 2 and 3 are also out of the network, so their data are not included.

Figure 5. Comparison between average turnaround times in 2007 and 2008 for all laboratories against the 15 working days recommended TRT.

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Table 1. Mean turnaround times (working days) of the laboratory network in 2007 and 2008.

Authorised Authorised Authorised Lanagro A Lanagro B Lanagro C Authorised Authorised Authorised Authorised

laboratory 1 laboratory 2 laboratory 5

laboratory laboratory laboratory laboratory

6 7 8 9

2007

2008

5 11 26 13 24 13 35 42 5 13

7 19 7 10 22 9 21 8 7 8

To date all the 31 analytical units in the Brazilian laboratory network that perform the analysis of residue and contaminants under the NRCCP are accredited to ISO 17025 by Inmetro, except for two units in Lanagro-PA and one in Lanagro-GO, which already have this quality system running and have been already evaluated primarily by Inmetro and are awaiting the issuing of the certificate. Table 2 shows only a few examples of participation in the PT rounds and the results achieved by the Brazilian network laboratories, either official or authorised. Acting as a reference for the network, the Lanagros have some duties and responsibilities for the performance of the network as a whole. Taking that into account, CGAL at the central level introduced a

Table 2. Examples of recent PT results of the National Agricultural Laboratory Network. Laboratory

Year

Lanagro-RS

2011

Provider CFIA – Canada

Assay Endectocides in swine liver

Four samples containing: Doramectin

Ivermectin

Lanagro-MG

2011

CFIA – Canada

Tetracyclines in Horse kidney

Four samples containing: Chlortetracycline

Oxytetracycline

Tetracycline

Lanagro-GO (assay not in PNCRC 2011 routine)

2011

CFIA – Canada

Tetracyclines in horse kidney

Four samples containing: Chlortetracycline

Oxytetracycline

Tetracycline

z-score Eight satisfactory z-score out of eight tests 0.91 –0.22 0.00 0.00 0.00 1.14 0.00 1.65 Twelve satisfactory z-score out of 12 tests –1.31 –1.55 –1.47 –0.24 –1.41 –1.40 0.00 0.00 –1.06 –1.05 –0.83 –0.55 Eleven satisfactory z-score out of 12 tests 1.57 2.22 1.32 0.60 1.10 1.75 0.00 0.00 1.67 1.57 1.24 1.00 (continued )

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Table 2. Continued. Laboratory

Year

Lanagro-SP

2010

Provider CFIA – Canada

Assay DES and zeranol in swine liver

Four samples containing: DES

Zeranol

Lanagro-MG

2010

CFIA – Canada

Sulfonamides in swine muscle

Four samples containing: Sulfadiazine

Sulfadimethoxine

Sulfamethazine

project to create in the Lanagros the capacity to produce reference materials and to provide PT rounds nationally and internationally. The first great outcome of that project was a ring test offered to the whole network and also to the Inter-American Network of Food Analysis Laboratories (INFAL). In total, four assays were offered (aflatoxins in Brazil nut, avermectins in bovine muscle, sulfonamides in pig liver, and inorganic contaminants in pig kidney), and 61 laboratories from 19 countries took part in the exercise. In 2012, the first Workshop of Residue and Contaminants in Food will take place, in which the Lanagros specialists will give lectures and present to the authorised laboratories the method validation criteria, QA/QC tools and others aspects that must be considered for network performance and the achievement of analytical excellence.

Conclusions The present work demonstrates that monitoring tools based on computerised systems are extremely useful to provide guidance for management initiatives that should be taken in order to optimise the operation of the NRCCP. From these data it can be concluded that some managerial actions have to be considered in order to improve the working conditions of the laboratories

z-score Eight satisfactory z-score out of eight tests 0.00 0.00 –1.84 –1.72 –0.99 –0.71 0.00 0.00 Eight satisfactory z-score out of eight tests 0.35 0.00 0.36 0.00 0.00 0.27 0.00 0.83 1.57 0.00 0.45 0.00

and the efficacy of the NRCCP itself. Conclusions given and points that lead to follow-up directrices are as follows: . By increasing the number of laboratories and spreading them along the five regions, taking into account the kind of analysis demanded for those regions and their herds, as well as equalising the seasonal sampling, it may lead to a better profile in laboratory performance in terms of turnaround times and efficiency. The concentration of analyses in a few laboratories should be avoided. . By managing and equalising the sample collect through the months and weeks, the routine of the laboratories can be normalised and allowed to develop a preparedness for events by diminishing ‘bottlenecks’ in sample reception. . Sample reception should have automated procedures as far as possible, giving the laboratories the possibility to be prepared to absorb the differences in the numbers of samples throughout the seasons. . The capacity of the laboratories located in the lower regions (South and Southeast) should be improved in order to respond to regional production demand. . Screening methods should be placed whenever possible.

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E.S. Lins et al. . National initiatives, such as PT provision, workshops and the improvement of the reference laboratories, should be encouraged. . Performance parameters for the network should be clearly defined and thoroughly observed to allow corrective actions. . Lastly, enforcement of the regulatory guidelines and timelines should be kept and assessed regularly in order to improve the service.

References BRASIL, Ministe´rio da Agricultura, Pecua´ria e Abastecimento, Secretaria de Defesa Agropecua´ria. 2008. Instruçaõ Normativa No 10/2008, Dia´rio Oficial da Uniaõ. Mauricio AQ, Lins ES, Alvarenga MB. 2009. A national residue control plan from the analytical perspective. Anal Chim Acta. 637:333–336.

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b-lactam antibiotics residues analysis in bovine milk by LC-ESI-MS/MS: a simple and fast liquid–liquid extraction method L. Jankabc*, R.B. Hoffabc, P.C. Taroucoab, F. Barretoabd and T.M. Pizzolatoc a

Laborato´rio Nacional Agropecua´rio – Lanagro/RS, Porto Alegre, RS, Brazil; bMiniste´rio da Agricultura, Pecua´ria e Abastecimento, Brazil; cInstituto de Quı´mica, Universidade Federal do Rio Grande do Sul – UFRGS, Porto Alegre, RS, Brazil; d Programa de Po´s-graduaçaõ em Cieˆncias Farmaceûticas, Universidade Federal do Rio Grande do Sul – UFRGS, Porto Alegre, RS, Brazil (Received 24 November 2010; final version received 28 June 2011) This study presents the development and validation of a simple method for the detection and quantification of six -lactam antibiotics residues (ceftiofur, penicillin G, penicillin V, oxacillin, cloxacillin and dicloxacillin) in bovine milk using a fast liquid–liquid extraction (LLE) for sample preparation, followed by liquid chromatographyelectrospray-tandem mass spectrometry (LC-MS/MS). LLE consisted of the addition of acetonitrile to the sample, followed by addition of sodium chloride, centrifugation and direct injection of an aliquot into the LCMS/MS system. Separation was performed in a C18 column, using acetonitrile and water, both with 0.1% of formic acid, as mobile phase. Method validation was performed according to the criteria of Commission Decision 2002/657/EC. Limits of detection ranged from 0.4 (penicillin G and penicillin V) to 10.0 ng ml 1 (ceftiofur), and linearity was achieved. The decision limit (CC ), detection capability (CC ), accuracy, inter- and intra-day repeatability of the method are reported. Keywords: chromatography – LC/MS; method validation; regulations; veterinary drug residues; veterinary drug residues – antibiotics; milk

Introduction With livestock development, milk from animal origin, mainly bovine milk, began to be produced for human consumption. Bovine milk is a rich source of important nutrients and is present in the human diet and derived processed foods. The number of dairy products derived from bovine milk has increased significantly in the last decades, which is 95% of the total dairy products (Michaelidou 2008), and they contribute substantially for the increase in demand. Dairy products contain a significant number of nutrients essential to growth and a healthy life. Industrialized products can increase this nutritive value by adding vitamins, minerals and other substances. Brazil is one of the biggest milk producers of the world; in 2009, its production was over 25 billion litres, an increase of around 2% in relation to 2008. It is estimated that milk’s production could potentially increase by 2.75% per year. This corresponds to an output of approximately 37 billion litres by the end of the projection, in 2019 (Ministe´rio da Agricultura, Pecua´ria e Abastecimento (MAPA) 2009). Milk agribusiness occupies a prominent position in the Brazilian economy, with great expectations for continually growing productivity for the next decade. *Corresponding author. Email: louisejank@gmail.com ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.604044 http://www.tandfonline.com

Milk quality is related to health, the management of animals and equipment during milking, the presence of microorganisms, drug residues and odours. Antibiotic residues present in milk are the result of the application of veterinary drugs, such as -lactams, for the prevention or treatment of diseases, especially infection of mammary gland and reproductive diseases. The presence of these substances in levels above the maximum residue level (MRL) renders milk unusable in dairy plants, as it makes the product unsuitable for use in industry and human consumption since there is no technological treatment that can inactivate these substances. The presence of -lactams antibiotics in milk may represent a risk to consumer health, such as allergic reactions and anaphylactic shock in sensitive individuals (Mendes et al. 2008), and their exposure may lead to an increase in the numbers of antibiotic resistant microorganisms. In the European Union, MRLs for this class of compounds in milk vary from 4.0 to 100.0 ng ml 1 (Table 1). For the Brazilian National Residue Control Plan (NRCP) (Mauricio et al. 2009), no MRL was set for the -lactams in milk until 2011. Currently, similar values to European Union MRLs were adopted. -lactam antibiotics have several pharmaceutical

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dosage forms for veterinary use in Brazil, with more than 160 registered products in 2011, of which 74 products have benzylpenicillin, also known as penicillin G, as the active compound. Sixty-eight of these benzylpenicillin products are injectable formulations, four are intra-mammarian infusions and two are ointments. In other words, the majority are for dairy cattle treatment. The same situation is observed in the case of cloxacillin, which has only injectable products, and with ceftiofur, which is available in seven injectable forms and one intra-mammarian infusion. Pharmaceuticals preparations containing -lactams antibiotics for bovine are summarized in Table 1. The widespread occurrence of -lactam antibiotics in milk has been reported in several publications (Kress et al. 2007). Several methods have been published in the past years using LC-MS/MS and other techniques (Mastovska and Lightfield 2008; Bailo´n-Pe´rez et al. 2009; Ortelli et al. 2009). -lactam antibiotics have poor stability in standard solutions and some specific compounds such as amoxicillin suffer degradation in solutions in 1 week or even fewer days. Due to the high polar characteristic of this class of compounds, chromatographic separation can be more complex and laborious than other antibiotic groups commonly analysed in milk, such as quinolones or sulfonamides. Generally, reversed-phase chromatography is used, but polar phases columns have been successfully used, as hydrophilic interaction columns (HILIC). Riediker et al. (2004) studied the stability of five -lactams in milk samples, and developed an extraction method that consists of a liquid–liquid extraction (LLE) with n-hexane followed by solid-phase extraction (SPE). A fast method was developed by Kantiani et al. (2009) to determine ten analytes in bovine milk (six penicillins and four cephalosporins), based on on-line solidphase extraction-liquid chromatography/electrospraytandem mass spectrometry (SPE-LC/ESI-MS-MS). Gaugain-Juhel et al. (2009) developed a method of screening 58 antibiotics, including penicillins, with two very short LLE, using acetonitrile for penicillins, cephalosporins, macrolides and sulfonamides and 5% trichloroacetic acid (TCA) solution for tetracyclines, quinolones, aminoglycosides and lincomycin. Another LLE method for detection of six penicillin residues, followed by an evaporation clean-up step, was carried by Feng et al. (2009). In order to prevent the degradation of penicillins throughout extraction, a derivatization reaction was proposed by van Holthoon et al. (2010). Their method used the precipitation of milk proteins with acids and clean-up of the supernatant in solid-phase extraction cartridge. Summarizing, -lactams analysis in milk are considered very complex mainly due to the low stability of this class of compound (Bittencourt 2003). Satisfactory results were obtained using extraction and clean-up techniques in tandem (i.e. LLE-SPE, SPE-SPE)

Table 1. European Community MRLs for -lactams antibiotics residues in bovine milk and the number of pharmaceutical dosage forms for veterinary use in milkproducing cattle in Brazil.

Compound

MRLa (ng ml 1)

Pharmaceuticalsb

4 4 4 30 30 30 50 50 100 100

24 11 74 – 13 – 6 – 13 8

Amoxicillin Ampicillin Penicillin G Oxacillin Cloxacillin Dicloxacillin Cefoperazone Cefazolin Cefalexin Ceftiofur

Notes: aEuropean Commission (1990). Data were obtained from the Sindan website, 2010 (National Union of Industry Products for Animal Health). Available from: http://www.cpvs.org.br/. b

(Turnipseed et al. 2008; Kantiani et al. 2010). However, these techniques require a more complex and more expansive analysis. For this reason, the present work aims to develop a fast and simple method for quantitative and confirmatory analysis of -lactams in milk samples. Extraction consists of a simple LLE protocol, using a small volume of sample and solvent, and a very fast chromatographic method. Chemical structure of -lactam antibiotics included in this work are shown in Figure 1.

Materials and methods Chemicals and reagents Ceftiofur (CFT), penicillin G (PNG), penicillin V (PNV), oxacillin (OXA), cloxacillin (CLX) and dicloxacillin (DCX) standards were obtained from SigmaAldrich Logistik (Scnelldorf, Germany) of >95% certified purity. Stock standard solutions were prepared by dissolving all compounds individually in 0.5% of polypropileneglycol 3000 in acetate buffer (pH 4.5), at a concentration of 0.5–3.75 mg ml 1, to make easier dissolutions to the work concentration pool. PNG and PNV stock solutions were diluted 1000-fold, to obtain 0.5 mg ml 1. OXA, CLX and DCX were also diluted 1000-fold to obtain 3.75 mg ml 1; to CFT, a dilution factor of 1:800 was applied to obtain a final concentration of 12.5 mg ml 1. Dilutions of stock solutions to prepare a pool were made with ultrapure water. Acetonitrile HPLC grade (ACN) and ammonium acetate were purchased from Merck (Darmstadt, Germany); formic acid (FA) was from J. T. Baker (Phillipsburg, NJ, USA). Deionized ultra-pure water (<18.2 M cm resistivity) was obtained from the

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Figure 1. Chemical structures of -lactams antibiotics: (1) generic structure of penicillins, where the R of analytes are described; and (2) structure of ceftiofur.

Milli-Q SP Reagent Water System (Millipore, Bedford, MA, USA). Sodium chloride and polypropileneglycol 3000 were obtained from Sigma.

LC-MS/MS The LC system consisted of an Agilent 1100 series LC (Santa Clara, CA, USA) with a quaternary pump, a vacuum degasser and an autosampler, coupled with an API 5000 triple quadrupole mass spectrometer (Applied Biosystems, Foster City, CA, USA). Chromatographic separation of compounds was performed using a SynergyĂ&#x2022; C18 HPLC column (150 3.0 mm; 4.0 mm), preceded by a security guard system C18, 5 mm, 4.0 3.0 mm, both obtained from Phenomenex. A binary mobile phase was used, with a flow of 500 ml min 1, in a total run time of 12 min. Mobile phase component A was ultra-pure water and component B was ACN, both with 0.1% of formic acid. The gradient optimised for the separation starts keeping 100% of A during 2 min; it then decreases linearly to 5% of A for 3 min and holds for 3 min. Finally, A increases linearly until 12 min to reach back to 100% of A. Electrospray ionization (ESI) in positive mode was used for the detection and quantification of targeted antibiotics. The TurboIonĂ&#x2022; spray voltage was set at 4000 V; and the temperature source at 300 C. Acquisition was performed in multiple reaction monitoring (MRM) mode to obtain sufficient quantification points to confirm each analyte.

Mass parameters were optimized by infusion of compounds, in a concentration of 250 ng ml 1, via a syringe pump at a flow rate of 10 ml min 1, in mobile phase (component A:component B, 50:50). After the identification of more abundant fragment ions for all compounds, as well as the ionization parameters for each particular transition, MRM chromatograms were obtained, indicating the retention order for the selected compounds. Flow injection analysis (FIA) was then performed for all compounds to optimize the conditions of ion source in the mass spectrometer: source temperature at 300 C, curtain gas (CUR) at 12 psi, ion spray voltage (IS) at 4000 V, ion source gas 1 (GS1) at 50 V and gas 2 (GS2) at 50 V, collision gas (CAD) at 4 V and entrance potential (EP) at 10 V. All data were processed by software Analyst version 1.4.2 (Applied Biosystems). MRM conditions, typical retention times and optimal declustering potential (DP), collision cell exit potential (CXP) and collision energies (CE) values are showed in Table 2 for all components, as well as typical product ions generated under these conditions.

Samples and sample extraction Five extraction methods were tested to identify which would be more adequate for present purposes. Details of these methods are described in the Results and discussion section. All tests were performed by spiking blank samples in order to obtain concentrations correspondents at MRL and 0.5 MRL values.

500

L. Jank et al. Table 2. Multiple reaction monitoring conditions. Precursor ion [M þ H]þ m/z

Transitions observed

DP (V)

CE (V)

CXP (V)

CFT

524

PNG

335

PNV

351

OXA

402

CLX

436

DCX

467

524 > 241a 524 > 210 335 > 176 335 > 160 351 > 114 351 > 160 402 > 160 402 > 243 436 > 160 436 > 277 467 > 114 467 > 160

126 126 96 96 86 86 96 96 101 101 106 106

25 79 17 17 45 19 19 19 19 19 47 19

16 10 20 18 16 22 16 26 16 30 24 16

Compound

Typical retention time (min) 7.30 7.57 7.70 7.82 7.95 8.10

Notes: aBold transitions are used for quantitative analysis. DP, declustering potential; CE, collision energy; CXP, collision cell exit potential.

Optimised extraction procedure consisted of subsequently adding four aliquots of 1.0 ml of ACN to a volume of 2.0 ml of milk sample, mixing in a vortex for approximately 10 s approximately between each addition. After this step, the sample was mixed in a headover-head shaker for 15 min, then 1.0 g of sodium chloride was added, and 15 min mixing more in the head-over-head shaker was carried out. Samples were then centrifuged for 5 min, at approximately 3000 g under refrigeration (5 C). Aliquots of supernatant were transferred to HPLC vials and submitted to LC-MS/MS analysis. A volume of 10 ml of extract was injected in the analytical system.

Stability of stock solutions A stability study to evaluate stock solutions was proposed. It consisted of a comparison of stock solutions prepared in water and in acetate buffer with different concentrations of polypropyleneglycol 3000 (PPG), a polymer that can provide some difficulty to -lactam degradation, probably because of pseudomicelle formation. PPG concentrations tested were 0.25%, 0.5%, 1.0%, 1.5% and 2.0%. Analytes’ concentrations were 1.0 mg ml 1 (CFT), 0.5 mg ml 1 (PNG and PNV) and 3.75 mg ml 1 (OXA, CLX and DCX). Solutions were diluted 1000-fold before every injection. Solutions were injected on the day of preparation, in the following 2 weeks and after that every 15 days.

Matrix effect The assessment of matrix effect was performed through the preparation and analysis of three calibration curves. Curve type I, called ‘curve in solvent’, was prepared by diluting the standard solution in the

mobile phase directly into the vial, giving a range of concentrations of 1–200 ng ml 1, according to the MRL for each substance. Curve type II, or ‘recovery curve’, was prepared by spiking blank samples with the desired amounts of -lactams, which were extracted and analysed as conventional samples. Curve type III, or ‘tissue standard curve’, was prepared by adding a -lactam standard solution in extracts of blank samples after extraction. All curves were made in the same range of concentrations.

Validation procedure Method validation was carried out following European Commission Decision 2002/657/EC (European Commission 2002) requirements for veterinary drug residue methods. Specificity, selectivity and stability were evaluated. Blank milk samples were spiked with -lactams at concentrations corresponding to 0.5, 1.0 and 1.5 MRL, in order to investigate parameters such as linearity, repeatability and reproducibility, as well as decision limit (CC ) and detection capability (CC ). For validation procedures, batches were composed by 21 spiked samples (seven for each concentration level: 0.5, 1.0 and 1.5 MRL), a calibration curve (0, 0.25, 0.50, 1.0, 1.5 and 2.0 MRL), and three ‘tissue standards’ (i.e. extracts of blank samples to which an amount of standard solution was added to obtain a concentration at MRL). This procedure was repeated three times on three different days.

Results and discussion Several experiments were performed for method optimisation, such as different chromatographic columns,

Food Additives and Contaminants different extraction methods and chromatographic conditions employed.

Sample extraction Despite the fact that the majority of authors in the literature adopted solid-phase extraction (SPE) to prepare milk samples, we chose to develop a method through liquid–liquid extraction (LLE) to reduce costs and, mainly, analysis time, making it simpler and easier, and preferentially utilising a small amount of solvent and sample. Although numerous solvents can be used to promote proteins precipitation in milk, as trichloroacetic acid, methanol or ethanol acidified, we chose ACN for solvent extraction to avoid the acid degradation of -lactams. Five extraction procedures were evaluated. Volumes of ACN addition, sample volume, saltingout effect, lipids removal and concentration by evaporation were investigated and optimised. . In extraction procedure 1 (EP1), 5 ml of milk were extracted with 10 ml of ACN. Extraction solvent was added in four aliquots of 2.5 ml and sample was vortexed between each addition for approximately 10 s. Tubes were mixed for 20 min in a mechanical shaker, followed by centrifugation for 5 min at 5000 g under refrigeration (5 C). Supernatant was evaporated, reconstituted in 1 ml of ACN:H2O (1:1) and injected in LC-ESI-MS/MS system. . For extraction procedure 2 (EP2), 2 ml of milk were extracted with 4 ml of ACN. Extraction solvent was added in four aliquots of 1.0 ml and sample was vortexed between each addition for approximately 10 s. Tubes were mixed per 20 min in a mechanical shaker and then 1.0 g of sodium chloride was added. Tubes were mixed for more 20 min followed by centrifugation for 5 min at 5000 g under refrigeration (5 C). An aliquot of supernatant (1 ml) was directly injected in LC-ESI-MS/MS system. . Extraction procedure 3 (EP3) was similar to EP2, but here just 2 ml of ACN were used (4 0.5 ml) without the addition of sodium chloride. In this protocol milk proteins do not precipitate and the resulting samples showed cloudy aspect. A small portion of ACN in a ratio of 1:1 with milk was insufficiently able to promote protein removal. . In extractions procedure 4 (EP4), using 2 ml of milk and 5 ml of ACN (2.0 þ 3.0 ml), 5 ml of chloroform were added after shaking step. The tubes were then shaken for 20 min more and centrifuged as in EP1 and EP2. The aqueous portion was removed, the

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supernatant was evaporated, it was reconstituted in 1 ml of ACN:H2O (1:1) and injected into the LC-ESI-MS/MS system. . For extraction procedure 5 (EP5), 2 ml of milk and 5 ml of ACN (2.0 þ 3.0 ml) were used following the same procedures as for EP2. In all tests, solvent (ACN) was added gradually to improve protein precipitation, and for the same reason samples were vortexed between additions. With the exception of EP 3, where no protein precipitation was observed, all others proved to be adequate, showing satisfactory recovery ( 60%). EP1, indeed, had a fivefold concentration factor and EP4 twofold. Although EP2 has no concentration factor, this protocol gave the best response. Furthermore, EP2 does not require an evaporation step, which provides the shortest analysis time of all experiments. For EP4, chloroform was tested to remove lipids and waterinsoluble sample components, but no advantage was perceived over EP1, EP2 or EP5. Sodium chloride was added in this method to saturate the aqueous phase and force organic compounds to migrate to the organic phase. After centrifugation, the organic phase was very clear, and an aliquot was injected directly into the LCMS/MS system, without a filtration step. EP2 demonstrates itself to be very effective, even with the advantage of using a small sample volume. Turnipseed et al. (2008) developed a method for several veterinary drugs residues in milk, including -lactams. Extraction was first performed with ACN (1 ml). Two additional steps of clean-up were proposed using SPE (OasisÕ HLB 3 ml 60 mg) followed by ultrafiltration trough a Microcon YM-30 centrifugal filter device (Millipore). Becker et al. (2004) published another similar method for the analysis of 15 -lactams in milk and kidney. For milk, specifically, the extraction protocol was very similar to the present approach using protein precipitation with CAN followed by a salting-out procedure. But final clean-up was also performed with Oasis SPE cartridges. SPE was also applied by Stolker et al. (2008) to analyse more than 100 veterinary drugs residues (including six -lactams) in milk. After ACN addition, supernatant was applied to a Strata-X SPE column. Our method has a lower scope, but the extraction procedure is complete without SPE or additional steps. As the overall aim was to develop a simple, fast and cheap extraction protocol, the present results were considered to be an achievement. The method can be easily applied for several samples in routine laboratories.

Stability of stock solutions The fast degradation of -lactams is well-established. Our tests to determine the expiry period for stock solutions showed a loss of analytes after 7 days when

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the solvent is pure water. In the presence of acetate buffer and PPG, degradation was only detectable in the second week. However, in concentrations more elevated with PPG (above 1.0%), some compounds (OXA, DCX and CLX) showed a decrease up to 60%. After 45 days, a rate of 60–90% of degradation was observed for all compounds in all solvents compositions. Moreover, when stock solutions were stored in aliquots (i.e. microcentrifuge tubes of 2 ml) under temperatures below 10 C, avoiding the exposing stock solutions to several cycles of frozen and refrozen at the moment of make dilutions, the stability was maintained for more longer periods. It was established in this test that stock solutions with 0.5% of PPG, stored in aliquots of 1–2 ml, showed the best stability, and can be stored for 6 months at a temperature under 10 C. However, work solutions should always be prepared at the moment of analysis to avoid the degradation of compounds in lower concentrations.

LC-MS/MS All compounds investigated showed an adequate response in positive ionisation mode (ESIþ), whereas PNG, OXA and DCX were satisfactorily ionised in both positive- and negative-ion modes. To proceed with the simultaneous analysis of all six compounds, a positive-ESI mode was chosen for all analytes. Experimental results showed formic acid 0.1% was the most suitable additive to enhance peak resolution and sensitivity. Acetic acid (0.1%) and ammonium formiate (5 mM) were also evaluated. As a stationary phase, two columns were tested: XTerraÕ C18 (100 2.1 mm, 3.5 mm) and SinergyÕ C18 (150 4.6 mm, 4.0 mm) which generated the best result. In X-Terra column, no effective retention was observed, with elution of analytes in the first 1 min of chromatographic analysis. Sinergy columns gave higher retention, which avoided co-elution of analytes with matrix polar co-extractives in the beginning of analysis, which generally cause enhancement of matrix effects under analytes, as signal suppression. Chromatographic separation of -lactams is a problem since due to the high polarity of these compounds they generally elute in the first minutes of a run, together with the dead volume of the columns. This is especially true for reverse-phase columns. Optimisation proceeded using a polar column, which promotes a more efficient analyte–column interaction. Indeed, the retention time window for all group of analytes is very small, which can lead to co-elution. Gradient mode was optimised to avoid possible co-elutions. The optimised chromatographic method has a total time of 12 min, which reliable for routine analysis since more samples can be analysed in a short period.

Chromatograms for all analytes with two monitored transitions are shown in Figure 2.

Validation procedure The validation procedure was carried out according to European Union Commission Decision 2002/657/EC requirements (European Commission 2002). Parameters considered more significant are described below.

Determination of limit of detection (LOD) and limit of quantification (LOQ) Considering that the mathematical approach to LOD and LOQ determination using the deviation of blank samples resulted in improbably low values, these parameters were established using data from spiked samples. To carry out the experimental determination of the lowest concentration detectable as required by guidelines for implementation of the European Union Decision (LOD and LOQ), calibration curves with lower concentrations than those used in previous tests (0.10 and 0.25 MRL) were analysed. The lowest spiked points were correctly identified and quantified. Based on these experimental data, LOD and LOQ were defined as 5% and 10%, respectively, of the MRL for each compound. LOD and LOQ values are presented in Table 3 as correlation coefficients, ranging from 0.9676 to 0.9992, which matches the internal criteria of our laboratory, which requires r2 > 0.95 for matrixmatched calibration curves.

Repeatability and reproducibility Repeatability and reproducibility data are summarized in Table 4. All values were satisfactory, considering the level calculated by the Horwitz equation. The coefficient of variation of repeatability (CVr) is acceptable when less than two-thirds the Horwitz CV. The CV for within-laboratory reproducibility (CVwIR) was performed by combined data obtained by three different analysts. CVwIR must be not higher than the Horwitz CV.

Accuracy Accuracy was determined using a comparison between the calculated concentration obtained by the matrixmatched calibration curve and the analyte amount added to the sample in the spiking procedure. The average accuracies obtained in three batches are listed in Table 5. In routine analysis, the accuracy determination for ‘tissue standard’ samples-type fortified in the value of the MRL was accompanied in each batch either for validation studies and analyses of routine or

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Figure 2. LLE-LC-MS/MS chromatograms for compounds at the MRL level concentration spiked milk sample (1 ¼ CFT, 2 ¼ PNG, 3 ¼ PNV; 4 ¼ OXA, 5 ¼ CLX and 6 ¼ DCX).

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Table 3. LOD and LOQ for -lactam antibiotics in milk.

LOD (ng ml 1) LOQ (ng ml 1) r2

CFT

PNG

PNV

OXA

CLX

DCX

10.0 25.0 0.9718–0.9956

0.4 1.0 0.9898–0.9982

0.4 1.0 0.9676–0.9980

3.0 7.5 0.9894–0.9992

3.0 7.5 0.9852–0.9986

3.0 7.5 0.9718–0.9956

Table 4. Repeatability and reproducibility data. Fortification levels (n ¼ 6 for each batch; three batches for each level) Analyte

Parameters

CFT

Average CVr CVwIR Average CVr CVwIR Average CVr CVwIR Average CVr CVwIR Average CVr CVwIR Average CVr CVwIR

PNG PNV OXA CLX DCX

0.5 MRL 49.5 6.3 6.9 2.0 5.8 6.3 2.0 4.5 7.4 13.7 7.3 10.9 15.4 6.4 6.3 15.2 8.7 9.1

1.0 MRL

52.0 6.5

51.3 8.1

2.1 2.2

2.2 7.9

2.3 6.6

2.1 4.9

16.5 5.3

16.9 5.2

16.8 3.7

16.0 5.5

17.5 2.6

15.1 5.7

100.5 13.8 9.5 4.2 14.0 8.9 4.2 10.7 9.9 28.1 10.2 12.4 29.9 6.4 10.1 31.0 8.5 11.0

1.5 MRL

101.9 4.0

109.9 7.3

4.3 5.9

4.1 5.5

4.6 6.4

3.9 4.2

36.2 6.5

31.9 2.1

36.2 5.8

30.8 1.1

37.0 5.9

30.4 2.9

129.2 6.3 11.1 5.4 4.7 10.2 5.6 10.1 14.2 36.6 10.2 19.9 38.0 11.2 19.8 38.5 11.3 21.8

156.5 7.8

155.6 7.0

6.6 7.5

6.0 4.5

7.3 7.9

5.8 5.5

57.8 7.4

48.0 2.6

58.4 7.2

45.1 3.4

60.0 10.7

43.9 2.1

Note: CVr, coefficient of variation (%) of the repeatability; CVwIR, coefficient of variation (%) of the within-laboratory reproducibility according to European Commission (2002). Table 5. Limit of decision (CC ), detection capability (CC ), accuracy and recovery data.

CFT PNG PNV OXA CLX DCX

MRL (mg l 1)

CC (mg l 1)

Accuracy average (%)a

Recovery (%)

100.0 4.0 4.0 30.0 30.0 30.0

120.4 4.7 4.7 36.5 35.6 36.3

147.9 5.7 6.1 53.7 52.8 56.6

104.1 107.0 105.0 106.9 107.8 109.9

41.9 54.2 63.7 73.8 79.3 81.3

Note: aAccuracies for the determination of samples spiked at the MRL (n ¼ 7): Accuracy (%) ¼ (calculated concentration/spike concentration) 100

for the construction of control charts for statistical tracking of the process, providing data for future estimation of the uncertainty of measurement. Tissuestandard samples were composed by extracts of blank samples in which a standard solution to obtain an MRL concentration in the final volume was added.

Recovery and matrix effect Area values found for the TS curve were more intense than those observed in the solvent curve. This fact can

be explained if one considers that the matrix in the study, milk, provides an increment to ionisation when compared only with the mobile phase. In some cases, such as oxacillin, the increment to ionisation is not so intense; on the other hand, ceftiofur presents considerable increases in the intensity of points on the TS curve in relation to the curve in solvent. Based on these data, recovery was calculated by considering that values found in the TS curve represent the intensity of the analyte considering the matrix effect, but without losses from the extraction process (thus, 100%)

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Figure 3. Matrix effect evaluation.

Table 6. ANOVAs for the comparison of raw and UHTtreated milk.

Analyte

Found value

Table value

Is there a significant difference?

CFT PNG PNV OXA CLX DCX

0.70 4.02 21.74 667.81 1318.25 41.33

12.22 12.22 12.22 12.22 12.22 12.22

No No Yes Yes Yes Yes

compared with the curve of the recovered which represents the signal observed after sample treatment. Recovery values calculated for each compound are shown in Table 5. Results for each compound are shown in Figure 3. Full validation was carried out with UHT milk. To evaluate if raw milk could present some matrix effect, three calibration curves, with eight points, were

prepared for each matrix (UHT and raw milk). For some compounds there was no significant difference between each kind of milk; however, for others the difference was very significant, presenting a signal twice that of the same level of concentration of one type of milk over another. When there was this difference, the UHT milk signal was larger than that from the raw milk, indicating that the raw milk had increased the ion suppression in comparison with milk treated industrially. An ANOVA test was performed for UHT and raw milk, as shown in Table 6; plots for each compound are presented in Figure 4. For this reason, it is mandatory to make calibration curves in blank samples with the same kind of milk (raw or UHT, for instance) that will be analysed.

Application to real samples The present method was used to analyse 84 raw milk samples, collected in several regions of Brazil. Just one non-compliant sample was detected, containing 7.9 mg l 1 of penicillin G.

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Figure 4. Matrix effect according to milk treatment: RAW, raw milk; UHT, UHT-treated milk.

Conclusion Almost all currently used methodologies for the analysis of -lactams antibiotics residues use SPE as clean-up procedure. The development of an LLE method as sample pretreatment is of great value since it becomes cheaper by avoiding the use of SPE cartridges, and faster, which are very important factors on deciding what method should be adopted for routine analysis. The method reported here has high sensitivity and gives satisfactory results for the identification and quantification of six -lactams antibiotics in bovine milk. The LODs are well below the MRLs set by the European Union for all compounds. The present method is currently included in the Brazilian National Residues Control Plan. Acknowledgements The authors would like to thank the National Council for Scientific and Technological Development

(CNPq) for the fellowships provided to L. Jank and P. Tarouco.

References Bailoń-Pe´rez MI, Garcı´ a-Campanã AM, del Olmo-Iruela M, Ga´miz-Gracia L, Cruces-Blanco C. 2009. Trace determination of 10 -lactam antibiotics in environmental and food samples by capillary liquid chromatography. J Chromatogr A. 47:8355–8361. Becker M, Zittlau E, Petz M. 2004. Residue analysis of 15 penicillins and cephalosporins in bovine muscle, kidney and muscle by liquid chromatography-tandem mass spectrometry. Analyt Chim Acta. 520:19–32. Bittencourt MS. 2003. Cefixima: validaça˜so de me´todos analı´ ticos e estudo preliminar da estabilidade [master thesis]. Porto Alegre: Universidade Federal do Rio Grande do Sul. European Commission. 1990. Council Regulation (EEC) No. 2377/90 of 26 June 1990: laying down a Community procedure for the establishment of maximum residue limits

Food Additives and Contaminants of veterinary medicinal products in foodstuffs of animal origin. Off J Eur Comm. L224:1–8. European Commission. 2002. Commission Decision 2002/ 657/EC of 12 August 2002: implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off J Eur Comm. L221:8–36. Feng Q, Zheng WJ, Chen GL, Qiu SL. 2009. Determination of six penicillins residues in milk by LC-MS/MS. Chin J Antibiot. 36:248–351. Gaugain-Juhel M, Dele´pine B, Gautier S, Fourmond MP, Gaudin V, Hurtaud-Pessel D, Verdon E, Sanders P. 2009. Validation of a liquid chromatography-tandem mass spectrometry screening method to monitor 58 antibiotics in milk: a qualitative approach. Food Addit Contam. 26:1459–1471. Kantiani L, Farre´ M, Freixedas JMG, Barcelo´ D. 2010. Development and validation of a pressurised liquid extraction liquid chromatography-electrospray-tandem mass spectrometry method for -lactams and sulfonamides in animal feed. J Chromatogr A. 1217:4247–4254. Kantiani L, Farre´ M, Sibum M, Postigo C, Alda ML, Barcelo´ D. 2009. Fully automated analysis of -lactams in bovine milk by online solid phase extraction-liquid chromatography-electrospray-tandem mass spectrometry. Analyt Chem. 81:4285–4295. Kress C, Seidler C, Kerp B, Schneider E, Usleber E. 2007. Experiences with an identification and quantification program for inhibitor-positive milk samples. Analyt Chim Acta. 586(1–2):275–279. Mastovska K, Lightfield AR. 2008. Streamlining methodology for the multiresidue analysis of -lactam antibiotics in bovine kidney using liquid chromatography-tandem mass spectrometry. J Chromatogr A. 1202(2):118–123. Mauricio AQ, Lins ES, Alvarenga MB. 2009. A national residue control plan from the analytical perspective – the Brazilian case. Analyt Chim Acta. 637(1–2):333–336.

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Mendes CG, Sakamoto SM, Silva JBA, Leite AI. 2008. Pesquisa de Resı´ duos de Beta-Lactaˆmicos no leite cru comercializado clandestinamente no municı´ pio de Mossoro´, RN, utilizando o Delvotest SP. Arquivos do Instituto de Biologia. 75:95–98. Michaelidou AM. 2008. Factors influencing nutritional and health profile of milk and milk products. Small Ruminant Res. 79:42–50. Ministe´rio da Agricultura, Pecua´ria e Abastecimento (MAPA). 2009. Projec¸o˜es dos Agronego´cios – Brasil 2008/09 a 2018/19. Brası´ lia: MAPA. p. 30–31. Ortelli D, Cognard E, Jan P, Edder P. 2009. Comprehensive fast multiresidue screening of 150 veterinary drugs in milk by ultra-performance liquid chromatography coupled to time of flight mass spectrometry. J Chromatogr B. 23:2363–2374. Riediker S, Rytz A, Stadler R. 2004. Cold-temperature stability of five -lactams antibiotics in bovine milk and milk extracts prepared for liquid chromatography-electrospray ionization tandem mass spectrometry analysis. J Chromatogr A. 1054:359–363. Stolker AAM, Rutgers P, Oosterink E, Lasaroms JJP, Peters RBJ, van Rhijn JA, Nielen MWF. 2008. Comprehensive screening and quantification of veterinary drugs in milk using UPLC-ToF-MS. Analyt Bioanalyt Chem. 391:2309–2322. Turnipseed SB, Andersen WC, Karbiwnyk CM, Madson MR, Miller KE. 2008. Multi-class, multi-residue liquid chromatography/tandem mass spectrometry and confirmation methods for drug residues in milk. Rapid Comm Mass Spectrom. 22:1467–1480. van Holthoon F, Mulder PPJ, van Bennekom EO, Heskamp H, Zuidema T, van Rhijin HA. 2010. Quantitative analysis of penicillins in porcine tissues, milk and animal feed using derivatisation with piperidine and stable isotope dilution liquid chromatography tandem mass spectrometry. Analyt Bioanalyt Chem. 396: 3027–3040.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 508–516

High-throughput multiclass screening method for antibiotic residue analysis in meat using liquid chromatography-tandem mass spectrometry: a novel minimum sample preparation procedure M.S. Bittencourta*, M.T. Martinsab, F.G.S. de Albuquerquea, F. Barretoab and R. Hoffac a Ministe´rio da Agricultura, Pecua´ria e Abastecimento, Laborato´rio Nacional Agropecua´rio – LANAGRO/RS, Porto Alegre, RS, Brazil; bFaculdade de Farma´cia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; cInstituto de Quı´mica, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil

(Received 23 November 2010; final version received 13 July 2011) A multiresidue and multiclass method based on liquid chromatography-tandem mass spectrometry for the determination of antibacterials was developed and validated for screening purposes. This method can be applied to commonly used drugs in veterinary medicine such as tetracyclines, quinolones and sulfonamides. Sample preparation consists in cell disruption with sand (previously purified and washed with EDTA 100 mM) followed by protein precipitation with acidified acetonitrile. Validation was conducted in accordance to European Union requirements (2002/657/EC) for qualitative methods covering detection capability (CC ), selectivity, specificity and stability. The method enabled the detection of 21 different drugs and had a false-compliant rate of 55% ( error) at between 25% and 50% of the maximum residue levels established by legal authorities. The methodology was successfully applied to incurred poultry samples. Keywords: chromatography – LC/MS; extraction; in-house validation; method validation; veterinary drug residues – antibiotics; veterinary drug residues – fluoroquinolones; veterinary drug residues – sulphonamides; veterinary drug residues – tetracycline; meat; animal products – meat

Introduction Antibacterials are substances frequently used for the prevention and treatment of diseases in cattle and poultry management. Tetracyclines, quinolones and sulfonamides are the most commonly used antibacterial groups for these purposes and may leave residues in edible tissues that could be associated with public health problems (Stolker et al. 2007; Gaugain-Juhel et al. 2009). In many countries, governmental authorities have established monitoring programmes to determine antibacterials in foods, as well as the highest allowable residue levels. Regarding residues of veterinary drugs in foodstuffs of animal origin, maximum residue limits (MRLs) were set by the Codex Alimentarius and/or regional or local authorities and monitoring plans were set up for ensuring MRLs and prevent the presence of prohibited substances in food. These MRLs are generally in range of 25–300 mg kg 1 for most common antibacterials; however, they go up to 400 mg kg 1 for flumequine in chicken muscle, for example (Boscher et al. 2010). An important tool to monitor closely and ensure this compliance in Brazil is the National Residue Control Plan (NRCP) (Mauricio et al. 2009). For this purpose, several analytical methods were applied. Microbiological assays have been most commonly used to analyse such residues; the *Corresponding author. Email: michele.bittencourt@oi.com.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.606228 http://www.tandfonline.com

advantages of these methods are the possibility of detecting a wide number of compounds simply and at a low cost. However, in some cases these methods are not sensitive enough (false-negative results) and are not really specific (false-positive results) (Gaugain-Juhel et al. 2009). Liquid chromatography (LC) coupled with mass spectrometry (MS) or tandem mass spectrometry (MS/MS) has become the most powerful technique for the determination of antibacterials in food matrices. LC-MS/MS, in particular LC triple-quadrupole (QqQ) MS/MS, has today become the technique of choice in antibacterial residue analysis. European Commission Decision 2002/657/EC states that methods based only on chromatographic analysis without the use of molecular spectrometric detection are not suitable for use as confirmatory methods (Bogialli and Di Corcia 2009). Over the last decade LC-MS/MS has become an essential technique for food analysis (Chico et al. 2008). Several papers have been published in recent years dealing with this issue. Most reported multiresidue methods target a few closely related compounds, usually belonging to a single drug class (Stolker et al. 2007). The cost-effectiveness of analytical procedures is becoming an important issue for all laboratories involved in the residue analysis of contaminants in food. An alternative to improve

Food Additives and Contaminants cost-effectiveness is to maximize analyte numbers that may be determined by a single portion of test material (Bogialli and Di Corcia 2009). Granelli and Banzell (2007) developed a screening method for detecting 19 antibacterials from five different classes in muscle and kidney. The scope of this work was extended to quantification and confirmation for the same compounds in muscle (Granelli et al. 2009). A multiclass antimicrobial determination using pressurized liquid extraction and LC-MS/MS for 31 drugs in beef was developed, but not including sulfamethoxazole, sulfamerazine, sulfamethazine, sulfachlorpyridazine, sarafloxacin, difloxacin, oxolinic acid and nalidixic acid which are considered very relevant for the NRCP (Carretero et al. 2008). Chico et al. (2008) reported the validation of a multiresidue method for 39 antibacterials in poultry meat and applied this method to several animal species using water and methanol as extraction solvents and ultra-high-pressure-liquid chromatography coupled with MS/MS. Another method for the determination of antibacterials in poultry meat based on QuEChERS methodology was applied to a large number of substances (Stubbings and Bigwood 2009). Martos et al. (2010) developed a method using liquid– liquid extraction (LLE) and LC-MS/MS for the determination of antibacterials. Recently, a multiclass method for detecting and quantifying veterinary drug residues in feedingstuffs was developed (Boscher et al. 2010). The aim of the present study was to develop and validate a simple, fast and inexpensive multiresidue screening method for the determination of 21 antibacterials in meat (cattle and poultry) using LC-ESIMS/MS in positive-ion mode. These drugs are included in NRCP for meat matrices. Validation was conducted for screening purposes based on European Commission Directive 2002/657/EC with measurements of detection capability (CC ), stability, specificity and applicability. The proposed method presented adequate compound separation, a simple extraction procedure, and a detection capability (CC ) between 25% and 50% of maximum residues level established by legal authorities, having a false-compliant rate of 55% ( -error).

Materials and methods Materials and reagents Analytical standards of sulfadimethoxine (SDMX), sulfaquinoxaline (SQX), sulfadiazine (SDZ), sulfachlorpyridazine (SCP), sulfathiazole (STZ), sulfapyridine (SPY), sulfamerazine (SMR), sulfamethoxazole (SMA), sulfamethazine (SMZ), chlortetracycline (CTC), tetracycline (TC), oxytetracycline (OTC), doxicycline (DOX), oxolinic acid (OXO), nalidixic acid (NALID), flumequine (FLU), ciprofloxacin

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(CIPRO), difloxacin (DIFLO), enrofloxacin (ENRO), norfloxacin (NOR), and sarafloxacin (SARA) were purchased from Riedel-de-Haen (Buchs, Switzerland) or by Sigma-Aldrich (St. Louis, MO, USA) as a powder. Stock standard solutions of each compound were prepared by dissolving 10 mg of analytical standard in 10 ml of appropriate solvent (methanol for tetracyclines, sulfonamides and quinolones; methanol with some drops (approximately two) of NaOH 1 M for fluorquinolones). Aliquots of each stock solution were diluted to obtain final concentrations of 10 and 1 mg ml 1 and were stored at 20 C. Formic acid was obtained from J.T. Baker (Phillipsburg, NJ, USA); methanol and acetonitrile (HPLC grade) were purchased from Merck (Darmstadt, Germany). All water used was ultra-pure deionized water produced by a Milli-Q apparatus (Millipore, Bedford, MA, USA). Di-sodium ethylenediamine tetracetate (Na2EDTA) was obtained from Sigma. Sand was purchased from Merck or was home treated by purification of sea sand (USP 1995), and washed with EDTA 100 mM. Sea sand (40–200 mesh size) was purified by calcination at 500 C for 2 h and washed three times with hydrochloric acid:water (1:2), using elution by gravity. The sand was then dried at 100 C for 4 h with periodic mixing and stored until analysis. At the day of analysis, portions of sand were treated with EDTA 100 mM, in a proportion of 1:2 (w/v). EDTA-sand was just gently dried at room temperature for 12–24 h to avoid decreasing in the metal chelating action (Bogialli et al. 2006). Blank samples were obtained from previously analysed samples and obtained in local markets.

LC-MS/MS instrumentation LC-MS/MS measurements were carried out using a Waters Alliance 2795 system coupled to a Quattro Micro triple quadrupole mass spectrometer from Micromass (Waters) with an electrospray source. Separation was achieved on a Symmetry C18 LC column (75 4.6 mm; 3.5 mm particle diameter) from Waters. A Phenomenex C18 (4.0 3.0 mm) was used as a guard column. The flow rate used was 400 ml min 1 and the column temperature was set at 20 C. A gradient elution programme was used with solvent A (aqueous solution 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid) as follows: 98% A (5 min), 98–80% A (2 min), 80% A (3 min), 80–50% A (1 min), 50% A (4 min), 50–98% A (2 min), kept at 98% A for 17 min returning to the initial composition, and held for 3 min to equilibrate the column. Mass analysis conditions optimisation were achieved on infusion injection at a flow rate of 10 ml min 1. Each standard solution was prepared

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separately in methanol with formic acid 0.1% at 1 mg ml 1. Source block temperature was set at 120 C in positive-ion mode with a capillary voltage of 3.0 kV. Nitrogen gas was used as a desolvation agent and nebuliser gas (N2) at flow rates of 400 and 50 l h 1, respectively. Argon was used as the collision gas. Detection was operated in multiple reaction-monitoring (MRM) mode. Instrument control and data processing were carried out by means of Masslynx 4.1 software purchased from Micromass.

Meat samples Meat samples (muscle) of cattle and poultry were homogenised to a semi-solid consistency using a food processor (Oster, Sunbean Products, Inc., USA) and stored at 18 C until analysis. Samples were kept at room temperature until defrosted and a portion of 6 g was weighed into a 50 ml polypropylene centrifuge tube. Spiked samples were prepared by adding the proper amount (150 and 300 ml) of a working solution containing all analytes (1 mg ml 1). Samples were stirred and allowed to stand for 10 min before extraction.

Methods Extraction procedure Extraction of antimicrobial agents from meat was performed through cell disruption by mixing 6 g of chopped and homogenised muscle sample with 4 g of sand (previously washed with EDTA) using a glass stick. To this mixture an aliquot of 250 ml of EDTA 100 mM was added and sample was homogenised in vortex for 30 s. Then, an aliquot of 600 ml of methanol was added (for spiked samples, just the necessary amount of methanol was added in order to complete 600 ml, considering methanol added in spiking solution). After that the mixture was vortexed for another 30 s and placed in an ultrasonic bath for 10 min at maximum power. Following, samples were centrifuged for 30 min at 3000 g (5 C). An aliquot of supernatant (800 ml) was transferred to a microtube containing 400 ml of acid formic 0.1% in acetonitrile, vortexed for 15 s and centrifuged for 30 min at 12 000 g (5 C). The supernatant was transferred to another microtube containing 600 ml of initial mobile phase (formic acid 0.1% in water:formic acid 0.1% in acetonitrile – 98:2) and centrifuged for 20 min at 12,000 g (5 C). The final supernatant was directly placed to HPLC vial and analysed by LC-MS/MS. Method validation An in-house validation procedure was conducted in accordance with European Commission Directive

2002/657/EC for screening purposes. According to these criteria one method can be validated and was used for screening purposes when there is a falsecompliant rate of 55% ( -error) at the level of interest. In the case of a suspected non-compliant result, it must be confirmed by a suitable confirmatory method (Ortelli et al. 2009). The proposed method in this work was mainly dedicated to screening and included parameters as follows: specificity, detection capability, applicability and stability. Selectivity/specificity Specificity was assessed by analysis of blank cattle and poultry muscles (n ¼ 20) for each matrix. Detection capability (CC ) Detection capability (CC ) was determined is the concentration at which the method can detect truly contaminated samples with a statistical certainty of 1 (false-compliant results were 5%). In a batch composed by 20 samples, this means a minimum of 19 samples with analyte detection and only one (5%) cannot be detected. In the case of substances with MLR, CC was determined by analysing meat samples spiked at 25% and 50% of MLR level. For each level, 20 samples were spiked and analysed in the LC-MS/ MS system. Stability It is well known that an inadequate or long-time storage for standard solutions may result in degradation products and, consequently, poor responses causing results deviations. Stability must be taken in account during the validation of residue methods. To evaluate the stability of standard solutions, dilutions of stock solutions were prepared with all analytes at 100 ng ml 1 in water and acetonitrile (98:2) with 0.1% formic acid, and stored at 20 C (n ¼ 10) and at 4 C (n ¼ 10), and at room temperature in dark vials (n ¼ 10) and at room temperature in normal vials (n ¼ 10). Samples were analysed in the LC-MS/MS system weekly and measured values were compared with those of freshly prepared standard solutions. Results were summarised for 3 months of evaluation.

Results and discussion Sample preparation The aim was to develop a fast and simple screening method for meat samples (cattle and poultry) able to detect the most used veterinary antibacterials. Muscle is composed of fibres, connective tissues, adipose tissue, cartilage and bone (Aerts et al. 1995). Extraction must be adequate to avoid interference

Food Additives and Contaminants of these substances. The procedure described here uses the high specificity and sensitivity of LC-MS/MS to simplify sample preparation. Briefly, extraction was conducted in order to obtain a fast and environmentally friendly protocol. The use of solid disruptors together with centrifugation and low-volume organic solvent provided, respectively, tissue homogenisation, tissue ‘juice’ liberation and protein precipitation. For this last purpose, tests were conducted using acetonitrile and methanol as organic solvent. Despite the propensity of methanol to extract excessive matrix material, this solvent was chosen in the first step, because all analytes were extracted with equivalent recoveries using methanol (Anderson et al. 2005). Ethanol with acetic acid (3%) was tested providing the cleanest extracts compared with procedures using only methanol, but results for tetracyclines were poorer. As the major objective was to disrupt tissues using dispersion with sand to liberate intracellular and interstitial liquid, centrifugation was tested in order to separate liquid containing analytes from solid debris. Increasing the centrifugation time (from 20 to 30 min at 3000 g) was important in providing greater liquid separation. After generic sample preparation with methanol, acidified acetonitrile and methanol (formic acid 0.1%) were also used before centrifugation and the presence of acid provided better results, especially for fluorquinolones. Acetonitrile in this step gave the cleanest extract and showed fewer coextracted endogenous compounds in comparison with methanol. Final centrifugation in the presence of mobile phase gave additional extract clean-up. The chelation of tetracyclines with multivalent cations are an important consideration as there is a high propensity for forming these complexes. In biological matrices divalent ions can interfere with the extraction and disruption of these interactions, which is commonly achieved through addition of ethylenediaminetetraacetic acid (EDTA) (Kawata et al. 1996). EDTA addition is necessary in order to prevent chelates formation by tetracyclines. An ultrasonic bath was considered to improve cell disruption and 10 min was established as an adequate time. The great advantage of the proposed method is the use of sand as an external promoter of interstitial fluids, as a real sample may contain contaminants or drug residues in its interior. The use of EDTA-treated sand was previously reported for the determination of tetracyclines in animal tissues (Blasco et al. 2009). Comparison of two different sands (Merck or sea sand previously purified in our laboratory) demonstrated that both are interchangeable and give similar results. Diatomaceous earth and polyethylene glycol were also tested, but these techniques did not provide a good release of interstitial fluids, necessitating the use of cartridge and filtering material to remove the liquid, which increased analysis costs and the time involved.

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Extraction with sand, which can be considered as a matrix solid-phase dispersion (MSPD) method, is not followed by elution or packaging in cartridges. As only centrifugation was used to obtain a liquid fraction enriched with analytes, this extraction protocol is not exhaustive. Thus, just a portion of total analyte mass was extracted. In order to determine the recovery, spiked samples were compared with ‘tissue standard’ samples, i.e. sample extracts spiked after completing the extraction process at a level equal to that expected considering no losses through analysis. Recoveries were in the range from 19% to 29%. To achieve higher recoveries, additional steps would need to be applied, but they would decrease the advantages of fast preparation and low cost. Considering this, we chose to validate this method as a qualitative method. In terms of sample preparation, the entire process can be performed for a batch of 25–30 samples in 3 h. Low-cost, low-solvent consumption and speed were factors which were prioritised in the development work. Several multiresidue methods published in recent years are more specific and/or more comprehensive in terms of the number of analytes. Bogialli et al. (2006) developed a MSPD method for tetracyclines analysis using sand as support but followed by a heated water extraction which required a specific apparatus. Yamada et al. (2010) published a multiresidue screening method for 130 veterinary drug residues in bovine, porcine and chicken muscle, but the extraction procedure required 50 ml of acetonitrile:methanol (95:5) and 30 ml of n-hexane per sample. In terms of low cost, the present method is comparable with the method of Chico et al. (2008) which used only 10 ml of methanol:water (70:30) as an extraction solvent. Other multiresidue methods require SPE or to split intermediary extract in aliquots to achieve satisfactory results (Stubbings and Bigwood 2009; Boscher et al. 2010).

Mass spectrometry To achieve maximum sensitivity, mass spectrometry parameters were optimised by direct infusion of standard solution with each analyte in methanol with formic acid (0.1%). A protonated molecular ion [M þ H]þ was selected as a precursor ion for all compounds, and the cone voltage was adjusted to its maximum signal at the first quadrupole of the mass spectrometer. Product ion spectra were recorded at different collision energies to find two most intense transitions for each analyte (Table 1). The ESI(þ) mode was chosen because of its sensitivity to all compounds studied in this work. Identification of individual antibacterials was based on chromatographic retention time, a characteristic qualifier ion and a confirmatory ion (Table 1). For sulfonamides,

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Table 1. Mass spectrometry parameters. Compound (MW, g mol 1) Sulfonamides Sulfadiazine (250.2) Sulfadimethoxine (310.2) Sulfapyridine (249.2) Sulfamethoxazole (253.3) Sulfaquinoxaline (299.2) Sulfathiazole (255.32) Sulfamerazine (264.31) Sulfamethazine (277.2) Sulfachlorpyridazine (284.7) Quinolones Sarafloxacin (385.3) Norfloxacin (319.3) Difloxacin (399.9) Ciprofloxacin (331.3) Enrofloxacin (359.3) Oxolinic Acid (261.2) Nalidixic Acid (232.2) Flumequine (261.2) Tetracyclines Doxicycline (444.4) Oxytetracycline (460.4) Tetracycline (444.4) Chlortetracycline (478.0)

CVa (V)

Transition 1 (CE2, eV)

Transition 2 (CE, eV)b

20 35 25 21 35 25 25 20 25

251.25 4 156.1 (15) 311.25 4 156.20 (20) 250.20 4 156.0 (15) 254.30 4 156.10 (20) 301.10 4 156.10 (15) 256.20 4 156.1 (15) 265.30 4 156.0 (15) 279.0 4 156.10 (20) 285.0 4 108 (25)

251.25 4 91.7 (25) 311.25 4 108.0 (25) 250.20 4 92.0 (25) 254.30 4 91.90 (25) 301.10 4 91.90 (30) 256.20 4 92.0 (28) 265.30 4 92.0 (30) 279.0 4 124.0 (20) 285.0 4 156 (15)

40 30 40 35 35 30 22 30

386.40 4 342.30 (20) 320.30 4 276.30 (17) 400.30 4 356.30 (20) 332.20 4 288.0 (17) 360.40 4 316.40 (20) 262.20 4 244.3 (18) 233.4 4 187.3 (26) 262.25 4 202.0 (30)

386.40 4 299.30 (28) 320.30 4 233.30 (20) 400.30 4 299.30 (28) 332.20 4 245.40 (23) 360.40 4 245.30 (25) 262.20 4 160.0 (25) 233.4 4 215.3 (15) 262.25 4 244.25 (20)

35 22 25 30

445.4 4 428.25 (20) 461.25 4 426.25 (20) 445.25 4 410.30 (20) 479.0 4 154.0 (30)

445.4 4 153.9 (30) 461.25 4 443.25 (12) 445.25 4 154.10 (30) 479.0 4 97.5 (40)

Notes: aCone voltage. b Collision energy.

two characteristic fragment ions at m/z 156 and m/z 92 were observed. The former corresponds to the common molecular fragment for all sulfonamides, p-sulfoaniline moiety and the latter corresponds to the loss of sulfonyl group from this structure (Chico et al. 2008). For sulfonamides, transition m/z 108 was also observed and corresponds to a loss of SO from psulfoaniline. Tetracyclines have a structure formed by an octahydrotetracene 2-carboxamide and conventional fragmentation in MS/MS shows a similar fragmentation pattern, where major ions usually obtained correspond to losses of NH3 and H2O or both. Oxytetracycline presented ammonia and water loss and a correspondent m/z transition 426 that was demonstrated to be the most intense in this present method. For chlortetracycline and tetracycline we could observe transition m/z 154 as the most intense (Diaz-Cruz and Barcelo 2005; Petrovic et al. 2005). The most frequent loss for flourquinolones was CO2. For nalidixic acid, oxolinic acid and flumequine (quinolones) the most intense fragments observed were m/z 187, 160 and 202, respectively. The total ion chromatogram (TIC) of 20 antibiotics spiked into a poultry sample at 50 ng ml 1 is shown in Figure 1. Results of a typical MRM LC-MS/MS chromatogram of poultry muscle spiked with antimicrobials are illustrated in Figures 2(a) and 2(b). Owing to the diverse characteristics of drugs, especially

Figure 1. Total ion chromatogram of 21 antibacterials for poultry muscle spiked with 50 ng ml 1.

polarity, various LC conditions were tested to achieve adequate separation. Methanol with formic acid was previously tested, but acetonitrile was best principally for tetracyclines, like most published methods. The gradient programme was based on a high proportion of aqueous phase in the first 5 min to extend the retention time for analytes to separate interferences from matrix products. In the second step in the gradient programme we used a greater portion of organic solvent (20% and subsequently 50%). However, simultaneous analysis of compounds from different groups with quite different physicochemical

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Figure 2. (a, b) MRM of 21 antibacterials in poultry muscle at 50 ng ml 1.

characteristics requires a compromise in the selection of experimental conditions, which in some cases are not the best conditions for all the analytes studied. These conditions provided elution of all analytes within 17 min. After this time, the mobile phase ratio of A:B was converted to an initial value (i.e., 98:2) to re-equilibrate the column. End-capping of the column on reversed-phase columns was preferred to improve

peak shapes, avoiding interactions with silanol groups, especially for tetracyclines (Anderson et al. 2005). Validation procedure Selectivity/specificity For the specificity study the absence of background peaks with a signal-to-noise ratio 43 at the retention

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Analytes

Figure 3. Total ion chromatogram of cattle blank sample.

time of analytes showed that the method was not affected by interference from endogenous compounds. Two transitions choices for each analyte provide specificity for this method. No interference peaks were observed within the retention time window for all substances. A TIC chromatogram for cattle blank samples is shown in Figure 3. Detection capability (CC ) Detection capability (CC ) was established for each analyte as the level in which false-compliant results were 5%. Experimental data showed CC values between 25 and 50 ng g 1; the results are given in Table 2, together with the MRLs valued adopted by the Brazilian NRCP. Detection capabilities were considered satisfactory, taking into account that a real sample with an analyte at the MRL will be correctly detected. Just for two compounds (SARA and NALID) were CC values not adequate for poultry samples analysis. Stability The assessment of stability guarantees the use of stock solutions at least for 2 months, when stored at 20 C and for 1 month at 4 C in dark glass bottles. Samples maintained at room temperature, but protected from light, are stable at least for 1 week, and this characteristic is important during analysis. Samples left in transparent vials at room temperature showed some degradation, especially for tetracyclines. A summary of the most relevant stability results is shown in Table 3. Results show that ENRO, TETRA and NALID had higher losses when comparing the storage of standard solutions for 3 months at 20 C. For the particular case of NALID precipitation was observed. For solutions maintained at room temperature in amber and clear vials OXI had the highest losses, with a 31% decrease in amber vials and 84% in clear vials.

Sulfadimethoxine Sulfaquinoxaline Sulfadiazine Sulfachlorpyridazine Sulfathiazole Sulfapyridine Sulfamerazine Sulfamethoxazole Sulfamethazine Doxicycline Chlortetracycline Tetracycline Oxytetracycline Oxolinic acid Nalidixic acid Flumequine Ciprofloxacin Difloxacin Enrofloxacin Norfloxacin Sarafloxacin

MRL (mg kg 1)a

(mg kg 1), cattle muscle

(mg kg 1), poultry muscle

100 100 100 100 100 100 100 100 100 100 200 200 200 100b 20b 500 100 100 100 100 202

25 25 25 25 25 25 25 50 25 50 50 50 50 25 25 25 50 50 25 25 25

Notes: aMRL values were adopted by the Brazil National Residues Control Plan (BRASIL 2010). b Adopted only for poultry muscle.

Tetracyclines are well known to form 4-epimers, which results in double peaks in the chromatograms. Generally, MRLs for tetracyclines are given as the sum of the parent compound and 4-epimers plus metabolites. In the present method, mild extraction conditions do not lead to the formation of 4-epimers, probably because extracts are not exposed to high temperatures or extreme pHs, which are the usual conditions in several extraction methods. The stability of the extracts was not assessed, since the maximum storage period for these samples is stipulated in the NRCP that requires a maximum of 15 days for the receipt, analysis and reporting of results (Blasco et al. 2009; Hoff et al. 2009). Ruggedness and method applicability The applicability and robustness of the described multiresidue LC-ESI-MS/MS are defined as the susceptibility of an analytical method to changes in experimental conditions, either minor changes such as solvent and reagents from different batches or major changes such as operator and matrix (Cherlet et al. 2003). Different batches of solvents and reagents during this period of tests did not cause interferences in the present study. Different analysts have performed sample extraction without affecting the results.

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Table 3. Stability data for some analytes (just analytes with significant alterations are shown). Signal loss (peak area, %) Analyte

RS1 versus RS2a

ART1 versus ART2b

CRT1 versus CRT2c

RS versus ART

RS versus CRT

ART versus CRT

ENRO TETRA NALID SMA SDZ SARA SCP OXI FLU SQX SMZ CIPRO STZ CLOR SDMX SMR SDX DOXI

11 22 39e n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

n.d.d n.d. n.d. 11 14 14 23 31 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

n.d. 11 11 13 12 n.d. n.d. 84 12 14 14 16 24 n.d. n.d. n.d. n.d. n.d.

n.d. 45 62 37 71 41 46 95 n.d. 39 32 22 34 44 15 35 16 23

n.d. 57 74 39 67 25 37 99 30 46 27 30 43 60 29 39 39 44

n.d. 22 32 n.d. n.d. n.d. 16 74 32 11 n.d. n.d. 13 29 17 n.d. 27 27

Notes: Solutions assigned as ‘1’ were freshly prepared and those assigned as ‘2’ were prepared 3 months later. a RS ¼ refrigerated standard solution ( 20 C). b ART ¼ amber vial at room temperature. c CRT ¼ clear vial at room temperature. d n.d., No difference; loss or gain lower than 10%. e Precipitation occurrence under storage.

The method was applied to samples from different producers, demonstrating its suitability. The method was successfully applied to the analysis of samples of poultry (n ¼ 26) and cattle muscle (n ¼ 21), including analysis for a considerable number of samples in 1 day (n ¼ 30). Two incurred poultry samples were included and quantified in specific methods for ENRO and SQX (Figures 4a and b). In addition, a proficiency test for sulfonamides in cattle muscle was performed including screening and quantitative methods (Progetto Trieste 2010, 2nd Round, Veterinary Drug Residues). Sulfadimethoxine was correctly detected by this present method and calculated as containing 59.0 mg kg 1. The z-score obtained in this test was 0.35 (satisfactory values are from 2 to þ2). Moreover, the method was recently accredited under ISO 17025 by the National Institute of Metrology, Standardization and Industrial Quality (INMETRO) under CRL 0384 (accreditation certificate).

Conclusions An LC-MS/MS method for the screening of 20 veterinary drug residues in meat samples was developed and validated. Although several multiclass methods for veterinary drugs have been published in recent years, in many cases the sample preparation

Figure 4. MRM of incurred poultry samples with sulfaquinoxaline and enrofloxacin.

is complex and laborious. In this paper a novel MSPDlike method was developed, using sand and some millilitres of organic solvents, to provide a fast, very cheap and environmentally friendly protocol, which was successfully applied to naturally incurred samples. This method was also applied to official analysis, and screenings results obtained for proficiency material were in agreement with the results from quantitative

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and confirmatory analysis. Recently, the present method was recommended for accreditation under ISO 17025 by the Brazilian Accreditation Body (INMETRO). The method is capable of satisfactory use for meat samples containing sulfonamides, tetracyclines and fluorquinolones, and is an important tool in the Brazilian National Residue Control Plan to monitoring antibacterial residues in cattle and poultry meat samples.

References Aerts MML, Hogenboon AC, Brinkman UAT. 1995. Analytical strategies for the screening of veterinary drugs and their residues in edible products. J Chromatogr B. 667:1–40. Anderson CR, Rupp HS, Wu WH. 2005. Complexities in tetracyclines analysis – chemistry, matrix extraction, cleanup, and liquid chromatography. J Chromatogr A. 1075:23–32. Blasco C, Di Corcia A, Pico Y. 2009. Determination of tetracyclines in multi-species animal tissues by pressurized liquid extraction and liquid chromatography-tandem mass spectrometry. Food Chem. 116:1005–1012. Bogialli S, Curini R, Di Corcia A, Lagana A, Rizzuti G. 2006. A rapid confirmatory method for analyzing tetracycline antibiotics in bovine, swine and poultry muscle tissues: matrix solid-phase dispersion with heated water as extractant followed by liquid chromatography-tandem mass spectrometry. J Agric Food Chem. 54:1564–1570. Bogialli S, Di Corcia A. 2009. Recent applications of liquid chromatography-mass spectrometry to residue analysis of antimicrobial in food of animal origin. Anal Bioanal Chem. 395:947–966. Boscher A, Guignard C, Pellet T, Hoffmann L, Bohn T. 2010. Development of a multi-class method for the quantification of veterinary drug residues in feedingstuffs by liquid chromatography-tandem mass spectrometry. J Chromatogr A. 1217:6394–6404. BRASIL. 2010. Ministe´rio da Agricultura, Pecua´ria e Abastecimento. Secretaria de Defesa Agropecua´ria. Instruc¸a˜o Normativa no. 08/2010. Carretero V, Blasco C, Pico´ Y. 2008. Multi-class determination of antimicrobials in meat by pressurized liquid extraction and liquid chromatography-tandem mass spectrometry. J Chromatogr A. 1209:162–173. Cherlet M, Schelkens M, Croubels S, Backer P. 2003. Quantitative multi-residue analysis of tetracyclines and their 4-epimers in pig tissues by high-performance liquid chromatography combined with positive-ion electrospray ionization mass spectrometry. Anal Chim Acta. 492:199–213. Chico J, Ru´bies A, Centrich F, Companyo´ R, Prat MD, Granados M. 2008. High-throughput multiclass method for antibiotic residue analysis by liquid chromatographytandem mass spectrometry. J Chromatogr A. 1213:189–199. Diaz-Cruz MS, Barcelo D. 2005. LC-MS2 trace analysis of antimicrobials in water, sediment and soil. Trends Anal Chem. 24:645–657.

European Commission. 2002. Commission Decision 2002/657/EC. Implementing Council Directive 96/23/EC concerning the performance of analytical methods and interpretation of results. Off J Eur Comm. L221:8–36. Gaugain-Juhel M, De´lepine B, Gautier S, Fourmond MP, Gaudin V, Hurtaud-Pessel D, Verdon E, Sanders P. 2009. Validation of a liquid chromatography-tandem mass spectrometry screening method to monitor 58 antibiotics in milk: a qualitative approach. Food Addit Contam. 26(11):1459–1471. Granelli K, Branzell C. 2007. Rapid multi-residue screening of antibiotics in muscle and kidney by liquid chromatography-electrospray ionization-tandem mass spectrometry. Anal Chim Acta. 586:289–295. Granelli K, Elgerud C, Lundstrom A, Ohlsson A, Sjoberg P. 2009. Rapid multi-residue analysis of antibiotics in muscle by liquid chromatography-tandem mass spectrometry. Anal Chim Acta. 637:87–91. Hoff RB, Barreto F, Kist TBL. 2009. Use of capillary electrophoresis with laser-induced fluorescence detection to screen and liquid chromatography-tandem mass spectrometry to confirm sulfonamide residues: validation according to European Union 2002/657/EC. J Chromatogr A. 1216:8254–8261. Kawata S, Sato K, Nishikawa Y, Iwama K. 1996. Liquidchromatographic determination of oxytetracycline in swine tissues. J. AOAC Int. 79(6):1463–1465. Martos PA, Jayasundara F, Dolbeer J, Jin W, Spilsbury L, Mitchell M, Varilla C, Shurmer B. 2010. Multiclass, multiresidue drug analysis, including aminoglycosides, in animal tissue using liquid chromatography coupled to tandem mass spectrometry. J Agric Food Chem. 58(10): 5932–5944. Mauricio AQ, Lins ES, Alvarenga MB. 2009. A national residue control plan from the analytical perspective – the Brazilian case. Anal Chim Acta. 637:333–336. Ortelli D, Cognard E, Jan P, Edder P. 2009. Comprehensive fast multiresidue screening of 150 veterinary drugs in milk by ultra-performance liquid chromatography coupled to time of flight mass spectrometry. J Chromatogr B. 877:2363–2374. Petrovic M, Hernando MD, Diaz-Cruz MS, Barcelo D. 2005. Liquid chromatography-tandem mass spectrometry for the analysis of pharmaceutical residues in environmental samples: a review. J Chromatogr A. 1067:1–14. Stolker AAM, Zuidema T, Nielen MWF. 2007. Residue analysis of veterinary drugs and growth-promoting agents. Trends Anal Chem. 26:967–979. Stubbings G, Bigwood T. 2009. The development and validation of a multiclass liquid chromatography tandem mass spectrometry (LC-MS/MS) procedure for the determination of veterinary drug residues in animal tissue using a QuEChERS (quick, easy, cheap, effective, rugged and safe) approach. Anal Chim Acta. 637:68–78. USP. 1995. USP 23 – The United States pharmacopeia. 23rd ed. Rockville (MD): USP. Yamada R, Kozono M, Ohmori T, Morimatsu F, Kitayama M. 2010. Simultaneous determination of residual veterinary drugs in bovine, porcine and chicken muscle using liquid chromatography coupled with electrospray ionization tandem mass spectrometry. Biosci Biotechnol Biochem. 70:54–65.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 517–525

Validation of a quantitative and confirmatory method for residue analysis of aminoglycoside antibiotics in poultry, bovine, equine and swine kidney through liquid chromatography-tandem mass spectrometry M.P. Almeida*, C.P. Rezende, L.F. Souza and R.B. Brito Ministry of Agriculture, Livestock and Food Supply (MAPA), Agricultural National Laboratory, Lanagro/MG, Brazil (Received 23 November 2010; final version received 31 August 2011) The use of aminoglycoside antibiotics in food animals is approved in Brazil. Accordingly, Brazilian food safety legislation sets maximum levels for these drugs in tissues from these animals in an effort to guarantee that food safety is not compromised. Aiming to monitor the levels of these drugs in tissues from food animals, the validation of a quantitative, confirmatory method for the detection of residues of 10 aminoglycosides antibiotics in poultry, swine, equine and bovine kidney, with extraction using a solid phase and detection and quantification by LC-MS/MS was performed. The procedure is an adaptation of the US Department of Agriculture, Food Safety and Inspection Service (USDA-FSIS) qualitative method, with the inclusion of additional clean-up and quantification at lower levels, which proved more efficient. Extraction was performed using a phosphate buffer containing trifluoroacetic acid followed by neutralization, purification on a cationic exchange SPE cartridge, with elution with methanol/acetic acid, evaporation, and dilution in ion-pair solvent. The method was validated according to the criteria and requirements of the European Commission Decision 2002/657/EC, showing selectivity with no matrix interference. Linearity was established for all analytes using the method of weighted minimum squares. CC and CC varied between 1036 and 12,293 mg kg 1, and between 1073 and 14,588 mg kg 1, respectively. The limits of quantification varied between 27 and 688 mg kg 1. The values of recovery for all analytes in poultry kidney, fortified in the range of 500–1500 mg kg 1, were higher than 90%, and the relative standard deviations were lower than 15%, except spectinomycin (21.8%). Uncertainty was estimated using a simplified methodology of ‘bottom-up’ and ‘top-down’ strategies. The results showed that this method is effective for the quantification and confirmation of aminoglycoside residues and could be used by the Brazilian programme of residue control. Keywords: animal products – meat; veterinary drug residues – antibiotics; chromatography – LC/MS

Introduction The aminoglycosides (AMGs) are antibiotics widely used in veterinary medicine due to their efficacy against Gram-negative bacilli and their positive synergism with other antibiotics in treating infections by Grampositive agents. These antibiotics have their use approved in Brazil. Their use in the country, however, is controlled as part of a veterinary drugs residue control programme for poultry, swine, equine and bovine (Brazil 1999) in an effort to keep the residues at levels considered safe for the consumers. The AMGs are stable at pH 6–8, highly soluble in water, have a cationic polar structure, are widely used in the treatment of respiratory and enteric bacterial infections, but should be used with criteria due to its nephrotoxic activity in humans (Kennedy et al. 1998; Bogialli et al. 2005; Oliveira et al. 2006). Figure 1 shows the chemical structures of the AMGs. Various

methods for the analysis of AMGs in biological samples are described in the literature, including indirect ultraviolet (UV) techniques, fluorescence and GC-MS/MS with derivatisation. The latter has the disadvantages of being time-consuming, with instability of the products and the generation of sub-products in the reaction. LC-MS/MS does not need the derivatisation step, provides more sensitivity and chromatographic efficiency, and has the possibility of confirmation (McGlinchey 2008; Zhu et al. 2008). The AMGs have a strong polar character, resulting in a poor retention in reversed-phase columns, low resolution and ion suppression due to co-elution of matrix components. To increase retention and separation one can use an ionic pair reagent (McLaughlin et al. 1994). Heptafluorobutyric acid (HFBA) is a common ionic pair reagent used in AMG analysis (Niessen 1998; Li et al. 2009).

*Corresponding author. Email: marcos.almeida@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.623681 http://www.tandfonline.com

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Figure 1. Molecular structures of the aminoglycosides.

The objective of this study was the validation of a quantitative and confirmatory multi-residue sampling method for the analysis of spectinomycin, hygromycin, streptomycin, dihydrostreptomycin, amikacyn, kanamycin, apramycin, tobramycin, gentamicin and neomycin in samples of swine kidney with solid-phase extraction (SPE) and detection/quantification using LC-MS/MS with the aim to comply with The National Control Plan of Residues and Contaminants (PNCRC) of Brazil. The method used as reference was obtained from the US Food Safety and Inspection Service

(FSIS-USDA). The original qualitative method was changed into a quantitative method and its validation was performed according to the European Commission 2002/657/EC (2002) criteria. Validation was subsequently extended to samples of poultry, equine, bovine kidney through the evaluation of inter-species matrix effects and comparison of linearity and CC and CC values obtained for each species. The uncertainty of the method was estimated from linearity and precision data using a simplified methodology composition the strategies of ‘bottom-up’ and ‘top-down’.

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Food Additives and Contaminants Materials and methods

Table 1. Gradient of mobile phase HPLC.

Chemicals and reagents Reference spectinomycin, hygromycin, dihydrostreptomycin, amikacyn, kanamycin, apramycin, tobramycin, gentamicin and neomycin standards with a minimum purity of 68% were obtained from SigmaAldrich (St Louis, MO, USA). A reference, streptomycin standard with purity of 99% was obtained from Riedel (Brazil). The following reagents were used: heptafluorobutyric acid (HFBA) (Fluka, Brazil), trichloroacetic acid (TCA) (Isofar, Brazil), acetic acid (HOAc) (AcroÂ´s Organics, Belgium), disodium of ethylenediaminetetraacetic acid (Na2EDTA) (Sigma), potassium phosphate monobasic (KH2PO4) (Sigma), and HPLC-grade methanol (Tedia, USA). The water was purified in a Milli-Q Gradient (Millipore, Brazil) system. A solid-phase extraction (SPE) cartridge BakerBond SPE Wide Pore CBX, 500 mg, 6 ml was supplied by J.T. Baker (USA).

Standard solutions Stock solutions of aminoglycosides standards at concentration of 200 mg ml 1 diluted in distilled deionised water were prepared and stored in freezer at < 12 C. Pools of all analytes, named addition solutions, were prepared at concentration of 10 mg ml 1 and stored at < 12 C. The solutions, stored in the above-mentioned conditions, should be used before 6 months (USDAFSIS 2005).

Blank samples Blank samples of poultry, swine, equine and bovine kidney species were obtained that gave negative results when screened by microbiological assay using the kit method FAST (USDA-FSIS 1998). Poultry blank samples, obtained from both kidneys of 200 animals, were crushed and homogenised. Swine, equine and bovine blank samples were obtained from one or more animals slaughtered in the same batch. Each sample was mixed to give a total of approximately 500 g.

Sample preparation A total of 2.00 0.10 g of kidney was weighed in a 50 ml polypropylene centrifuge tube. Samples were fortified with the addition solution (10 mg ml 1) at concentrations of 0.5, 0.75, 1.0, 1.25 and 1.5 MRL. To each tube was added 20 ml of a buffer containing potassium dihydrogen phosphate, TCA and EDTA. The tubes were homogenised in ultraturrax and centrifuged at 3000 g for 10 min (USDA-FSIS 2005). The supernatant was transferred to another centrifuge tube containing 5 ml of hexane, shaken for 5 min,

Time (min) 0.00 0.75 9.00 9.10 11.25 12.75 14.00 18.00

%A: Water

%B: Methanol HPLC

%C: HFBA 0.1 mol l 1

75.0 30.0 20.0 10.0 10.0 18.6 75.0 75.0

5.0 50.0 60.0 70.0 70.0 61.4 5.0 5.0

20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0

centrifuged at 3000 g for 5 min, and the upper layer of hexane was removed. The pH of the extract was adjusted to between 7.5 and 8.0. The solid-phase extraction (SPE) cartridge was conditioned using 5 ml of MeOH followed by 5 ml of distilled deionised water. The extract (20 ml) was passed through the cartridge under moderate flux. The analytes were retained and the buffer solution eluted. The cartridge was then washed with 5 ml of distilled deionised water and dried immediately. The retained analytes were eluted into a 15 ml-glass tube with 3 ml of HOAc/MeOH 10% and followed by 1 ml of MeOH. The eluted solution was evaporated under compressed air flux or nitrogen at 40 C ( 5 C) and the analytes were re-diluted with 500 ml of HFBA 5 mmol l 1. The solution was centrifuged at 14,000 rpm for 15 min and filtered on a 0.45 mm PTFE membrane.

LC-MS/MS analysis For detection and quantification of AMGs, a Waters LC-MS system, an Alliance 2795 HPLC system, and a Quatro Premier XE triple quadrupole mass spectrometer were used. Chromatographic separation was carried out using in a Waters X-Terra MS C18 3.5 m, 2.1 100 mm column coupled to a Varian Pursuit C18, 2 mm, 5 m pre-column, at 30 C, using a solution of water, methanol and HFBA 0.1 mol lâ&#x20AC;&#x201C;1 as mobile phase, with flux rate of 0.200 ml min 1. The gradient of the mobile phase is shown on Table 1. The total run time was 18 min for an injection volume of 20 ml. Detection was carried out by electrospray ionization in positive mode with a capillary voltage of 3.5 kV, source temperature of 100 C, desolvation temperature of 400 C, and cone and desolvatation gas flow rates of 20 and 500 l h 1, respectively. The transitions and collision energies used are detailed in Table 2.

Method validation The validation of this method followed the criteria and requirements of Decision 2002/657/EC (European

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Table 2. Transitions and collision energies for antibiotics.

Compounds

Ion Cone MRM Collision precursor voltage transition energy (m/z) (V) (m/z) (eV)

Spectinomycin

333.1

Hygromycin

528.3

Streptomycin

582.2

Dihydrostreptomycin

584.2

Amikacyn

586.2

Kanamycin

485.1

Tobramycin

468.2

Apramycin

540.1

Gentamicin

478.1

Neomycin

615.2

140 189 177 352 263 407 263 409 324 425 163 324 163 324 344 378 160 322 455 293

20 20 25 25 30 30 30 30 20 20 30 30 20 20 20 20 25 15 25 25

Commission 2002). In poultry kidney matrix the following parameters were determined: linearity band and working range, matrix effect, trueness, repeatability, intra-laboratory reproducibility, CC , CC and selectivity. For other species, a study of inter-species effect was undertaken. Maximum residue level (MRLs) for some AMGs and some species are shown in Table 3 (Codex Alimentarius Commission 2003). Those values were used in the experimental design. For some AMGs, however, the MRLs have not yet been established. Whereas the AMGs are not prohibited drugs, this study adopted a validation level for cases where no MRL is established. The validation level adopted for the experimental design was the lowest MRL established for an AMG of 1000 mg kg 1.

Analytes equivalent to blank matrix The validation procedure began with the extraction and purification of six poultry kidney blank samples to check for possible interference. One of the blank samples was fortified at the lowest level of 500 mg kg 1. The samples were injected and the blank samples readings were compared with the readings for the sample fortified at the lowest level.

Selectivity To evaluate the selectivity of the method, four blank samples were fortified at levels of concentration 0.50,

Table 3. MRLs set by the Codex Alimentarius Commission (2003) for aminoglycosides at species bovine (B), swine (S), poultry (P) and equine (E). Maximum residue level (MRL) (mg kg 1) Analytes Spectinomycin Hygromycin Streptomycin Dihydrostreptomycin Amikacin Kanamycin Tobramycin Apramycin Gentamicin Neomycin

5000 n.e. 1000 1000 n.e. n.e. n.e. n.e. 5000 10,000

5000 n.e. 1000 1000 n.e. n.e. n.e. n.e. n.e. 10,000

n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e.

Note: *Limits of reference; n.e., not established.

1.00 and 1.50 MRL. Two blank samples of each level were contaminated with a standard solution containing antibiotics of the macrolide group (lyncomicin, clindamycin, tilmicosin, erytromicin and tylosin). A direct standard curve was also prepared, in pure solvent, at concentrations equal to 0.50, 0.75, 1.00, 1.25 and 1.50 MRL. This experiment was repeated twice.

Linearity and working range The study of linearity and working range consisted of triplicate injections of standard solutions at concentrations 0.50, 0.75, 1.00, 1.25 and 1.50 MRL in 3 different days.

Matrix effect The matrix effect was evaluated by the fortification of five evaporated blank samples extracts with standard concentrations equal to 0.50, 0.75, 1.00, 1.25 and 1.50 MRL. A calibration curve was also prepared with the same levels of concentration. The samples were injected in triplicate and the experiment was repeated twice.

Repeatability The evaluation of repeatability of the method was performed with the extraction of 18 samples on the following levels of concentration: six blank samples fortified at 0.50 MRL, six blank samples fortified at 1.00 MRL, and six blank samples fortified at 1.50 MRL. This experiment was repeated twice.

Food Additives and Contaminants Determination of CC and CC The determination of these parameters was obtained by the analysis and extraction of five blank samples fortified at levels of concentration 0.50, 0.75, 1.00, 1.25 and 1.50 MRL. A blank sample was also analysed without fortification. The samples were injected in triplicate and this experiment was repeated in twice. These limits were calculated according to International Standards Organization (ISO) 11843 (1997) and (Meier and Zu¨nd 2000) using the calibration curve (Equations 1 and 2):

all analytes. Blank samples did not show interference and the equivalence in analyte was null, or relatively low if compared with samples fortified at the lowest level. Figures 2 and 3 show typical chromatograms. The instrumental response values in relation to the concentration obtained in the evaluation of linearity were estimated from the functional relationship, or the curve calibration equation, using the minimum square statistical method. The results showed that in the concentration range studied (0.5–1.5 MRL), the calibration data come from variables linearly corre-

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 0 !2 u PN u wx u i,j¼1 i i,j C B PN u C S2 ð y Þ w t5% u s2 ðy ÞLMR B 1 i,j¼1 i C B LMR u þ CC ¼ LMR þ þ B P 2C P C B N b u K k N w u wx A @ i,j¼1 i PN t i,j¼1 i i,j 2 PN i,j¼1 wi xi,j i,j¼1

where k is the number replicates; w is the weigh given by the ratio of the variances of each instrumental response; y is the instrumental response; x is the concentration; b is the slope; t5% is the tabulated t-value at 5% significance and 4 degrees of freedom; and S2(y) is the instrumental response variance: CC ¼ MRL þ 2CC

ð2Þ

Intra laboratorial reproducibility Reproducibility was evaluated by the execution of the repeatability experiment under differentiated conditions and with different analysts.

Inter-species effect To study the inter-species effect, 2.0 g of 24 kidney blank samples were weighed, six for each of swine, equine, poultry and bovine. Standard solutions were added to each sample so as to get a recovered curve for each species with the concentrations of 0.50, 0.75, 1.00, 1.25 and 1.50 MRL, and one blank sample. The blank samples were submitted to extraction and injected in triplicate; the experiment was repeated on two more occasions. The results were then evaluated as the linearity of the recovered curves for each species and inter-species variation by comparing the curves for each species with the curve for poultry.

Results and discussion The profile of ions typical of the analytes of interest and their relative intensity agree with Decision 2002/ 657/CE. The signal-to-noise ratio obtained was >3 for

521

ð1Þ

lated. The regressions coefficients (r2) for all the calibration curves used were 0.95. Different standard deviations for different concentrations levels (heteroskedasticity), which suggests a weighted linear regression model for the obtained spiked calibration curves, were also observed. The weighted regression model was used for the spiked calibration curves because a test of homogeneity of variance (F-test) detected that in the range adopted the data are heteroscedastic. The weighting was done considering the levels of minimum variance. The result of the selectivity study showed that the addition of possible interferent macrolides to the samples did not cause a significant effect on the chromatograms or on the recovery of the analytes. The t-test (95% of significance) was used for result comparisons. The matrix effect was obtained by comparing the responses of the curves of direct standards with those of fortified blank samples. The comparison of the mean analyte responses of the two calibration curves was obtained using the F-test (Snedecor’s) of variation homogeneity, and the t-test (Student’s) of comparison of means. The t-test was also used for the slope and intercept, which confirmed the matrix effect. The curves were compared in three distinct occasions (days). The results show there is a matrix effect between the direct standard curve and the fortified samples curve. Due to the significant result of the matrix effect, the samples were quantified using a matrix curve in all experiments and they showed satisfactory linearity. The trueness studies, repeatability and intralaboratorial reproducibility were obtained by two different analysts, who individually repeated the extraction of analytes at three levels of concentration

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Figure 2. Chromatogram of the blank sample for aminoglycoside.

(0.5, 1.0 and 1.5 MRL) with six replicates by level, resulting in 18 extractions per day, on three different days. The total extractions per analyst were 54. The quantification of all samples analysed in the

experiment to study repeatability was obtained using the curve of the fortified blank matrix (recovered). The results of the trueness study showed that the recovery presented values between 90% and 107% for

Food Additives and Contaminants

523

Figure 3. Chromatogram of the aminoglycosides spectinomycin, kanamycin, hygromycin, dihydrostreptomycin, streptomycin, amikacyn, tobramycin, apramycin, gentamicin, neomycin channels 1 to 10, respectively.

all analytes (Table 4), which was considered satisfactory according to the Codex Alimentarius Commission (2003), which showed values of 80â&#x20AC;&#x201C;110% of the studied levels. The results of the repeatability study showed that the relative standard deviation (RSD%) presented values <15%, under repeatability conditions, except

for spectinomycin, which was around 22%, but <23% (Table 4), under intra-laboratorial reproducibility conditions, which is the RSD% considered satisfactory by the Codex Alimentarius Commission (2003). Ishii et al. (2008) reported a method for the determination of aminoglycosides in kidney and meat using the matrix-matched standard curves to calculate the

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M.P. Almeida et al. Table 4. Recovery and RSD of the method.

Compounds Spectinomycin Hygromycin Streptomycin Dihydrostreptomycin Amikacyn Kanamycin Tobramycin Apramycin Gentamycin Neomycin

Concentration (mg kg 1)

Recovery (%)

500 1000 1500 500 1000 1500 500 1000 1500 500 1000 1500 500 1000 1500 500 1000 1500 500 1000 1500 500 1000 1500 500 1000 1500 500 1000 1500

87.7 97.0 95.0 93.8 95.6 101.7 104.9 101.1 99.7 106.8 99.7 97.9 105.9 101.8 95.3 98.7 99.2 101.6 99.7 97.6 94.9 107.3 102.7 97.9 104.2 102.9 98.0 91.6 95.5 99.4

recoveries of 10 replicates. They showed recoveries in the range of 70–110%, except for gentamicin, and the RSD% of repetibility and reprodutibility were lower than 11.8 and 13.8%, respectively. The measurement uncertainty estimate was obtained from the combination of uncertainties of the calibration curve and intra-laboratorial reproducibility, congruous to what is recommended by the topdown methodology, and for this study was not regarded the uncertainty of sampling. The uncertainty of the calibration takes into account the uncertainties of the intercept and slope, as the uncertainty of reproducibility is determined by the RSD under these conditions. The obtained measurement uncertainty, CC and CC are shown in Table 5. The study of the inter-species matrix effect was performed by comparison of calibration curves of fortified blank matrix (recovered) of poultry kidney with the same curves of swine, equine and bovine kidney. The results were compared using the F-test and t-test on three distinct occasions (days), presenting a matrix inter-species effect in all experiments. The results show there is a statistical difference between the curves of poultry kidney and those of the other

CV (%) repeatability (n ¼ 54)

CV (%) reproducibility (n ¼ 108)

20.5

21.8

10.6

11.9

5.0

7.6

8.2

11.7

10.2

11.6

9.8

9.6

8.1

10.2

9.5

11.3

15.8

15.0

10.5

12.3

Table 5. CC , CC and uncertainty measurement estimate determined for poultry kidney. Poultry kidney

Analytes

CC (mg kg 1)

Uncertainty estimate (mg kg 1)

Spectinomycin Hygromycin Streptomycin Dihydrostreptomycin Amikacyn Kanamycin Tobramycin Apramycin Gentamicin Neomycin

5663 – 1117.4 1181.0 – – – – 5096 10,466

6326 – 1234.7 1362.0 – – – – 5191 10,933

107.9 124.6 68.8 96.6 137.0 98.0 128.1 110.2 70.0 68.4

species, for all analytes. The results from the study of the inter-species effect allowed the calculation of CC and CC (Table 6) and the evaluation of the linearity of all curves. Despite the significant difference between the curves, this method can be used to analyse matrix

Food Additives and Contaminants Table 6. CC and CC for species bovine and swine. Bovine kidney

Analytes

Swine kidney

CC CC CC CC (mg kg 1) (mg kg 1) (mg kg 1) (mg kg 1)

Spectinomycin 5326 Hygromycin – Streptomycin 1036.2 Dihydrostreptomycin 1071.9 Amikacyn – Kanamycin – Tobramycin – Apramycin – Gentamicin 5269 Neomycin 10,367

7154 – 1073.3 1143.9 – – – – 5538 10,736

5474 – 1158.2 1198.6 – – – – 6549 12,293

5948 – 1316.3 1397.1 – – – – 8100 14,588

from other species because the curves showed satisfactory linearity and the quantification of real samples is carried out in a curve of the same species.

Conclusion The detection/quantification of aminoglycosides residues using liquid chromatography-tandem mass spectrometry proved to be a sensitive and specific technique for samples from poultry, swine, equine and bovine kidney. The proposed method separates ten aminoglycosides with a reasonable resolution in only one chromatographic run of 18 min. The validation steps executed meet fully the criteria and requirements of Decision 2002/657/EC (European Commission 2002). The results of the validation process show that this method is acceptable for application on the National Program of Residues Control and Contaminants (PNCRC) for monitoring of the levels of aminoglycoside residues by the Laboratory of Veterinary Medicaments Residues (LRM/PL) of LANAGRO-MG.

References Bogialli S, Curini R, di Corcia A, Lagana` A, Mele M, Nazzari M. 2005. Simple confirmatory assay for analyzing residues of aminoglycoside antibiotics in bovine milk: hot water extraction followed by liquid chromatographytandem mass spectrometry. J Chromatogr A. 1067:93–100. Brazil. 1999. Brasil – Dia´rio Oficial da Uniaõ, Instruçaõ Normativa N 42 de 20 de dezembro de 1999, publicada no D.O.U. n 244 de 22 de dezembro de 1999 Seçaõ 1. p. 213. Codex Alimentarius Commission. 2003. Report of the Twenty-sixth Codex Committee on Residues of Veterinary Drugs in Food, ALINORM 03/23 (2003).

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European Commission. 2002. Commission Decision 2002/ 657/EC of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Brussels (Belgium): European Commission. International Standards Organization (ISO). 1997. ISO 11843:1. Capability of detection – Part 1: Terms and definitions. Geneva (Switzerland): ISO. International Standards Organization (ISO). 2005. ISO/IEC 17025:2005: General requirements for the competence of testing and calibration laboratories Geneva (Switzerland): ISO. Ishii R, Horie M, Chan W, MacNeil J. 2008. Multiresidue quantitation of aminoglycoside antibiotics in kidney and meat by liquid chromatography with tandem mass spectrometry. Food Addit Contam. 25(12):1509–1519. Kennedy DG, McCracken RJ, Cannavan A, Hewitt SA. 1998. Use of liquid chromatography-mass spectrometry in the analysis of residues of antibiotics in meat and milk. J Chromatogr A. 812:77–98. Li B, Shepdael AV, Hoogmartens J, Adams E. 2009. Characterization of impurities in tobramycin by liquid chromatography-mass spectrometry. J Chromatogr A. 1216:3941–3945. McGlinchey TA, Rafter PA, Regan F, McMahon GP. 2008. A review of analytical methods for the determination of aminoglycoside and macrolide residues in foods matrices. Analyt Chim Acta. 624:1–15. McLaughlin LG, Henion JD, Kijak PJ. 1994. Multi-residue confirmation of aminoglycoside antibiotics and bovine kidney by ion spray high-performance liquid chromatography/tandem mass spectrometry. Biol Mass Spectrom. 23:417–429. Meier PC, Zu¨nd RE. 2000. Statistical methods in analytical chemistry. 2nd ed. Vol. 153. New York (NY): Wiley Interscience. Niessen WMA. 1998. Analysis of antibiotics by liquid chromatography-mass spectrometry. J Chromatogr A. 812:53–75. Oliveira JFP, Cipullo JP, Burdmann EA. 2006. Nefrotoxicidade dos aminoglicosı´ deos. Braz J Cardiovasc Surg. 21:444–452. US Department of Agriculture, Food Safety and Inspection Service (USDA-FSIS), Office of Public Health Science. 1998. Detection of antimicrobial residue by fast antimicrobial screen test (FAST). Available from: www.fsis. usda.gov/PFD/CLG_AMG_1_03.pdf US Department of Agriculture, Food Safety and Inspection Service (USDA-FSIS), Office of Public Health Science. 2005. Confirmation of aminoglycosides by HPLC-MS/MS SOP N CLG-AMG1.02. Available from: www.fsis.usda. gov/PFD/CLG_AMG_1_03.pdf Zhu W, Yang J, Wei W, Liu Y, Zhang S. 2008. Simultaneous determination of 13 aminoglycoside residues in foods of animal origin by liquid chromatography-electrospray ionization tandem mass spectrometry with two consecutive solid-phase extraction steps. J Chromatogr A. 1207:29–37.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 526–534

Occurrence of antimicrobial residues in Brazilian food animals in 2008 and 2009 C.K.V. Nonakaa, A.M.G. Oliveirab*, C.R. Paivab, M.P. Almeidab, C.P. Rezendeb, C.G.O. Moraesb, B.G. Botelhoa, L.F. Souzab and P.G. Diasb a

Bolsista CNPq, National Agricultural Laboratory, LANAGRO/MG, Brazil; bMinistry of Agriculture, Livestock and Food Supply, MAPA – National Agricultural Laboratory, LANAGRO/MG, Brazil (Received 25 November 2010; final version received 31 August 2011) Brazil is one of the most important countries as a producer and exporter of cattle and poultry. In 2009 cattle accounted for 30% of the export market and 41.4% for poultry meat. The Brazilian National Residues and Contaminants Control Plan (PNCRC) follows the guidelines set by the Codex Alimentarius Commission and checks compliance maximum residue limits (MRLs) to ensure the quality of these commodities. Kidney samples (n ¼ 2978) were analysed between January 2008 and December 2009. Fifteen antibiotics of the macrolide and aminoglycoside groups (clindamycin, eritromycin, lincomycin, tylmicosin, tylosin, amikacin, apramycin, dihydrostreptomycin, gentamycin, higromycin, kanamycin, neomycin, spectinomycin, streptomycin, tobramycin) were determined by a microbiological screening method (FAST) and confirmed/quantified using liquid chromatography (LC-MS/MS and UPLC-MS/MS). In 2008, 1459 samples were analysed by a screening test and liquid chromatography with only one sample (0.07%) exceeded Brazilian legislation limits (4MRL). In 2009, 1519 samples were analysed and none exceeding Brazilian legislation limits (4MRL). The slaughterhouses of 16 states were monitored during the year of 2008, and 18 states were monitored in 2009, being the major producing states most sampled by the PNCRC. Keywords: animal products – meat; meat; veterinary drug residues – antibiotics; screening – microbial screening; chromatography – LC/MS; chromatography – HPLC

Introduction Brazil is one of the most important producers and exporters of cattle and poultry in the world. The relationship between Brazilian exports and world trade shows that in 2009 the sales of cattle accounted for 30% of the market, 41.4% for poultry meat and 12.4% for pork (Avicultura Industrial 2009). Figure 1 shows the increase in meat production in Brazil since 1994, with a growth for poultry of 200.4%, for cattle of 76.9% and for pork of 127.5% (Associac¸a˜o Brasileira da Indu´stria Produtora e Exportadora de Carne Suı´ na (ABIPECS) 2009). The use of modern systems of planning, organisation along with new technologies has enabled a steady growth in meat production. Among these technologies are the antibiotics that can be used as growth promoters (low doses), treatment (high doses) and prevention (intermediate doses) of diseases in food-producing species (Viana 2000; Gomes 2004; Amaral et al. 2006; Brumano and Gatta´s 2009). Today it is estimated that more than half of all antimicrobials produced globally are used in livestock (Guardabassi and Kruse 2010). Thus, antibiotics and sulfonamides have been widely used, especially in poultry and swine, with the *Corresponding author. Email: andrea.garcia@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.625649 http://www.tandfonline.com

goal of improving animal performance by reducing production costs since it is difficult to keep the breeding environment free from pathogens. This use has been an excellent tool to help achieve high levels of productivity, where we see better growth rates and feed efficiency, a better performance index in addition to the reduction of mortality and morbidity (Santos et al. 2003). As a result, residues of these drugs or their metabolites may appear in the tissues and organs of those animals, which may pose an adverse health effect for consumers, especially when the use of antibiotics is above the maximum residue limits (MRLs). The consequences for consumers can be antibiotic-resistance pathogens and hypersensitive reactions to allergic drugs. The agriculture industry is subject to policies and bills that restrict, limit or forbid certain products as feeding additives. Many agriculture industries also obey prohibitions that are in force in other countries, especially in the European Union, in order to comply with the international market. According to IN No. 65 (Brasil 2006), Brazil it is not allowed to use nonauthorised drugs in animal feed. This is inspected by the Ministry of Agriculture, Livestock and Food Supply (MAPA).

Food Additives and Contaminants

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Figure 1. Evolution of meat production in Brazil from 1994 to 2007 by ABIPECS.

In Brazil the monitoring of animal products is accomplished through the National Residues and Contaminants Control Plan (PNCRC), which was established by MAPA in order to provide control for the presence of residues resulting from the use of veterinary drugs, agro-chemicals and environmental contaminants, thus ensuring the safety of food offered for consumption. The PNCRC has its actions directed to examine and avoid the non-conformity of safety standards or MRLs of permitted substances and the occurrence of any levels of chemicals residues banned for use in the country (Brasil 1999). It is emphasised that the main goal of the PNCRC is to check the correct and safe use of veterinary drugs in accordance with the required veterinary practices (knowledge on the antimicrobial withdrawal period) and technologies used in the process of increasing production and productivity of livestock. The programme thus involves whole government effort in order to offer consumers safe and quality food. A failure to meet the annual goals set out for the control of residues in meat, for example, will cause serious problems for Brazilian meat product exports to major markets (Brasil 1999). According to Maur覺織 cio et al. (2008) the evolution of the PNCRC has had a significant increase in the extent of its coverage. By 2006, approximately 50 analytes were monitored; in 2007 there were about 75 analytes; and in 2008 were 118 analytes. This increase of the number of substances within its scope is directly linked to the expansion of the analytical capability of the Brazilian laboratories, since only substances to which there are validated methods can be included in the PNCRC. The objective of this paper is to report the occurrence of residues of antibiotics (macrolides and

aminoglycosides (AMGs)) for 2008 and 2009 in the samples monitored by the PNCRC and analysed by the Laboratory of Residues Veterinary Medicine (LRM/ PL) of LANAGRO/MG, belonging the MAPA.

Materials and methods The numbers of samples to be analysed for each species per year are published in annual programmes. For 2008, the IN No. 10 (Brasil 2008) determined a total of 1440 samples (460 cattle, 460 poultry, 460 pigs, 60 horses) for the analysis of antibiotic residues (macrolides and AMGs) and in 2009 a total of 1490 samples (485 poultry, 485 pigs, 460 cattle, 60 horses) was determined in IN No. 14 (Brasil 2009). The sampling programme is based on the methodology recommended by the Codex Alimentarius Committee, a model of binomial distribution and in accordance with the prevalence of this group of residues in the target species. In the case of antibiotics used in Brazil, a confidence interval of 95%, determined in IN No. 42 (Brasil 1999), is used. The tolerance limits or MRLs set out in those ordinances have as a reference the Codex Alimentarius or European Union Directives. From January 2008 to December 2009, 2978 kidney samples of cattle, pig, poultry and horse were received by the LRM/PL. Of those, 1462 samples were received in 2008 and 1528 samples in 2009. From the samples received in 2008, three were discarded because there were different tissues from those used in the analysis, then 474 samples were sent directly to the screening and confirmation analysis by liquid chromatography, as there were not enough available kits (LC-MS/MS and UPLC-MS/MS). The other 985 samples were

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screened by the microbiological method FAST, with positive samples being forwarded for confirmation and quantification by LC-MS/MS and UPLC-MS/MS. In 2009, five samples were sent to liquid chromatography analysis under investigation as those were part of the investigation plan established by IN No. 24. The investigation samples were established by IN No. 24 whenever the producer has a violated sample, thus being monitored (five times throughout a period, in other words five samples from different production lots are collected) and under investigation until the investigation is complete (negative for all the five samples, or still kept under investigation). Production is also stopped until the results are available, therefore thwarting the producer from exporting the meat. Four other samples were discarded for not being the same tissue used in the screening. The remaining 1519 samples were analysed by microbiological screening, those positive being forwarded for confirmation and quantification by LC-MS/MS and UPLC-MS/MS. The samples were collected by the Federal Inspection Service, identified, frozen and properly packaged to arrive at LANAGRO/MG still frozen. At the Samples Reception Sector (REC/PL) only those that are under analysis are forwarded to the LRM/PL and the others are discarded, generating new lots for replacement. The microbiological screening method used was FAST MLG 33.5 (US Department of Agriculture, Food Safety and Inspection Service (USDA/FSIS) 1998). It is based on the principle that if animal tissue contains an antimicrobial residue previously administered, the fluid from the tissue will inhibit the growth of a bacterial culture plate with a sensitive organism. This organism is Bacillus megaterium ATCC 9885, which is known to be sensitive to most of the commonly used antimicrobials, presenting an inhibition of bacterial growth in the form of an inhibition zone as a response to microbial analysis. This method is capable of detecting a wide range of antimicrobial residues. The lyophilised strain of B. megaterium ATCC 9885 MicroBioLogicsÕ brand was used and the spore suspension was prepared as recommended in the technique. The plates with the culture media Mueller–Hinton agar (DifcoÕ ) were prepared by the Department of Preparation of Culture Media (PMC/PL) following the preparation instructions. For the group of AMGs, samples were analysed by UPLC-ESI-MS/MS. Initially, the AMG residues were extracted by phosphate buffer containing trichloroacetic acid as a precipitant of protein. The extract was neutralised and purification was carried out by solidphase extraction with a caution exchange cartridge and eluted with acidified methanol. The methanol extract was evaporated and reconstituted in an aqueous reagent ion pair. Confirmation of the analytes was based on the presence of fragments of specific ions for

each analyte and the retention time was compared with the standards. The following AMGs were analysed: diidroclorato spectinomycin (DHS), hygromycin (HYG), streptomycin (EST), sesquisulfate dihydrostreptomycin (SDS), amikacin (AMK), kanamycin (KAN), apramycin (APR), tobramycin (TOB), gentamicin (GEN) and neomycin (NEO). As for macrolides, the samples were analysed by LC-ESI-MS/MS. The analytes were extracted from the sample with phosphate buffer pH 8.0 and the extract was purified by cartridge solid-phase extraction. The analytes were eluted with a solution of acetonitrile/ ammonium hydroxide 98:2 v/v. The extract solvent was evaporated to dryness and the residue was dissolved in methanol/water 1:1 v/v. The confirmation was based on the presence of fragments of specific ions for each analyte and the retention time when compared with standards. Lincomycin (LIN) and the macrolides (clindamycin (CLI), erythromycin (ERI), tilmicosin (TIM) and tylosin (TYL)) were analysed. All methods were validated according to operational procedure standards for the validation of qualitative and quantitative methods developed by the LRM/PL from Decision 657/2002 (European Commission 2002), which provides parameters for the methods to be used in the analysis of residues. The validation results (Table 1) for the FAST method showed limits of detection (LOD) lower than the MRLs for the various analytes tested. In this way, the method was very sensitive to AMGs in fortified blank samples with standard solutions and for the macrolide the sensitivity of the method was not as high in fortified samples, but in naturally contaminated samples came up to levels of 240.0 mg kg 1 CC for LIN and 45.0 mg kg 1 for TIM. The validation of methods for the confirmation and quantification of liquid chromatography-high performance mass spectrometry (LC-MS/MS) for residues of antibiotics – macrolide and lincomycin and AMGs – was performed in the kidneys of poultry, cattle, horses and pigs. It used the MRLs established by Council Regulation No. 2377/90 (European Commission 1990). The results of the LOD, limit of quantification (LOQ), decision limit (CC ), detection capability (CC ) and the uncertainty estimate obtained in the validations are presented in Tables 2 and 3. For validation, CC and CC were established experimentally by using a reference limit of 1000 mg kg 1. For MRLs greater than the reference limit used, CC and CC were calculated mathematically.

Results and discussion The LRM/PL in these two years performed the entire programme for macrolide and AMG residues. To meet to this demand, it was necessary to use a very sensitive

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Table 1. Analytes, MRLs and LOD determined by the validation of the screening method (FAST) by the Laboratory of Residues Veterinary Medicine (LRM/PL, MG, Brazil). Values (mg kg 1) MRL (PNCRC) Analyte

Cattle

Horse

Pig

Poultry

LOD

Spectinomycin (DHS) Higromycin (HYG) Streptomycin (EST) Dihydrostreptomycin (SDS) Amikacin (AMK) Kanamycin (KAN) Tobramycin (TOB) Apramycin (APR) Gentamycin (GEN) Neomycin (NEO) Tylosin (TYL) Clindamycin (CLI) Erytromicin (ERI) Tilmicosin (TIM) Lincomycin (LIN)

n.e. n.e. 1000 1000 n.e. n.e. n.e. n.e. 5000 10,000 n.e. n.e. n.e. 1000 1500

n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e. n.e. 1000 1500

n.e. n.e. 1000 1000 n.e. n.e. n.e. n.e. 5000 10,000 n.e. n.e. n.e. 1000 1500

5000 n.e. 1000 1000 n.e. n.e. n.e. n.e. n.e. 10,000 n.e. n.e. 200 500 500

620a 5000b 200b 100b 100b 200b 50b 100b 50b 50b 10a 12a 50a 45a 240a

Notes: aNaturally contaminated samples. b Spiked tissues. n.e., Non-established.

Table 2. Analytes, MRLs, CC , CC , LOD, LOQ and uncertainty determined by the validation of the method for residues of macrolides and lincomycin by the Laboratory of Residues Veterinary Medicine (LRM/PL, MG, Brazil). Values (mg kg 1) Analyte Tylosin (TYL) Clindamycin (CLI)a Eritromycin (ERI) Tilmicosin (TIM) Lincomycin (LIN)

MRLb

LOD

LOQ

Uncertainty

100 200 200 1000 1500

114.8 218.0 218.8 1073.5 1644.3

129.5 236.0 237.6 1147.0 1788.5

10.8 4.0 1.4 39.2 106.9

12.3 4.6 1.6 44.7 121.6

14.4 15.6 13.2 70.6 123.3

Notes: aThe CLI of the MRL is not established; the reference value of erythromycin was used for this analyte. Source: EEC Regulation 2377/90 MRLs for residues of veterinary drugs in foods of animal origin.

Table 3. Analytes, values of MRLs, CC- , CC- , LOD, LOQ and uncertainty, determined on validation (poultry’s kidney) of the method for residues of aminoglycosides by Laboratory of Residues Veterinary Medicine (LRM/PL, MG, Brazil). Values (mg kg 1) Analyte Spectinomycin (DHS) Higromycin (HYG) Streptomycin (EST) Dihydrostreptomycin (SDS) Amikacin (AMK) Kanamycin (KAN) Tobramycin (TOB) Apramycin (APR) Gentamycin (GEN) Neomycin (NEO)

MRLa

LOD

LOQ

Uncertainty

5000 n.e. 1000 1000 n.e. 2500 n.e. n.e. 750 5000

5662.9 – 1117.4 1181.0 – 3405.3 – – 816.8 5138

6325.8 – 1234.7 1362.0 – 4310.7 – – 883.6 5275.1

543.7 22.5 113.4 90.5 243.0 90.8 68.6 227.5 235.6 184.7

618.7 25.6 129.0 103.0 276.5 103.3 78.0 258.9 268.1 210.2

107.9 124.6 68.8 96.6 137.0 98.0 128.1 110.2 70.0 68.4

Notes: aSource: Council Regulation EEC 2377/90 MRLs for residues of veterinary drugs in foods of animal origin. n.e., Non-established.

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Table 4. Total analysed kidney samples and the distribution of positive samples and confirmed (for one or more analytes) by species in 2008 by the Laboratory of Residues Veterinary Medicine (LRM/PL, MG, Brazil).

Table 5. Total analysed kidney samples and the distribution of positive samples and confirmed (for one or more analytes) by species in 2009 by the Laboratory of Residues Veterinary Medicine (LRM/PL, MG, Brazil).

Total Species screening Positives Macrolides Aminoglycosides

Cattle Pig Horse Poultry Total

463 468 63 465

56 190 23 95

13 122 7 56

2 33a 3 40

Cattle Pig Horse Poultry

1459

364

198

Total

469 487 60 503

92 384 51 117

13 187 6 41

0 8 1 14

1519

644

247

Note: aOne violation: SDS.

(with detection limits lower than the MRLs) screening method, thus allowing the analysis of many samples simultaneously. The method chosen was FAST, which is a biological screening test for the detection of antimicrobial residues in animal tissues. Dey et al. (2005) found that compared with the Swab Test On Premises (STOP) developed in 1977, the detection limits of FAST were significantly better. FAST showed sensitivity to chlortetracycline, tetracycline, oxytetracycline, erythromycin, tylosin, sufamethazine, gentamicin and neomycin. The distribution of antibiotic residues in 1459 samples analysed in 2008 are shown in Table 4. Of the 364 (25%) positive samples for the screening test (microbiological, LC-MS/MS and UPLC-MS/ MS), the presence of residues of macrolide was confirmed in 198 (14%) samples, and AMG residues were confirmed in 78 (5.35%) samples. In only one sample (0.1%) was a residue level above the established MRL found. Among non-violated positives, the six main analytes found in 2008 were LIN, ERI, TIM, GEN NEO and TYL (Table 6). It is noteworthy that in Brazil the antibiotics detected are used as growth promoters or for feed efficiency (pro-nutrient) to prevent and control diseases, particularly in poultry and pigs (Butolo 1999). As noted in Table 6, the number of positive cases is higher in pigs and poultry, and there is a prevalence of macrolide antibiotics and lincomycin. The macrolide group and the related lincosamides comprise antibiotics used for both therapy and performance enhancement in livestock production. The results of analysis in 2009 are shown in Tables 5 and 7. A total of 1519 samples were analysed, of which 644 (42%) were positive in the screening, 240 (16%) samples were non-violated positives for residues of macrolides, and for AMGs 23 (1.5%) samples were non-violated positives. No sample was found with results above the maximum allowed. In 2009, among non-violated positives, the six main analytes found were LIN, TIM, TYL, CLI, GEN and ERI (Table 7). A small change in the prevalence of

antibiotics between 2008 and 2009 can be noted, but the use of lincomycin was maintained for pig and poultry (Tables 6 and 7). According to Sundlof (2006), lincomycin is used in the treatment of swine dysentery (all lincomycin-susceptible organisms), for growth promotion and for improved feed efficiency in pigs and chickens, joint infections (infectious arthritis) in pigs (e.g. susceptible Staphylococcus spp., Streptococcus spp., Erysipelothrix rhusiopathiae and Mycoplasma spp.) and for the control of necrotic enteritis in chickens (e.g. Clostridium perfringens). An efficient screening method needs to be low cost with high throughput, and able to identify potential non-compliant samples from a large set of negative samples effectively. One of the problems of FAST is the high number of false-positive results and the fact that it is not adequate for detection of a broad spectrum of antibiotics such as beta-lactam and sulfonamides. The LRM/PL is seeking other methods for screening to increase the number of analytes to be covered by the PNCRC and to improve the specificity/selectivity of response in order to reduce the number of falsepositives and thus decrease the numbers of samples sent for confirmation by chromatographic methods. Since 2010 the method validated Kit PremiĂ&#x2022; Test has been routinely used in this laboratory along with the FAST. The PremiĂ&#x2022; Test was more sensitive for group macrolides detection, beta-lactams and sulfonamides. However, the FAST method has been more sensitive when detecting tetracycline and AMGs. The screening of the samples in 2008 was carried out by a microbiological method and liquid chromatography, thus increasing the number of positive samples in the screening. Chromatography is very specific for these groups (macrolides and AMGs). Moreover, in 2009 the number of registered producers who were part of the sampling plan was higher than in the previous year. However the percentage of samples confirmed and the analytes found were similar in both years. It is important to note that most of the results confirmed that the values found were below the LOQ

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Table 6. Results of the six major analytes found in 2008 by the Laboratory of Residues Veterinary Medicine (LRM/PL, MG, Brazil). Values (mg kg 1) Analytes

MRLa

Cattle

Horse

Pig

Poultry

Maximum found

Lincomycin (LIN) Eritromycin (ERI) Tilmicosin (TIM) Gentamycin (GEN) Neomycin (NEO) Tylosin (TYL)

1500 200 1000 5000 10,000 100

3 10 3 0 2 0

2 2 3 0 0 0

76 31 24 5 10 12

38 26 9 19 20 3

624 5LOQ 5LOQ 5LOQ 5LOQ 5LOQ

Note: aMRLs – CODEX and EEC 2377/90.

Table 7. Results of the six major analytes found in 2009 by the Laboratory of Residues Veterinary Medicine (LRM/PL, MG, Brazil). Values (mg kg 1) Analytes Lincomycin (LIN) Tilmicosin (TIM) Tylosin (TYL) Clindamycin (CLI) Gentamycin (GEN) Eritromycin (ERI)

MRLa

Cattle

Horse

Pig

Poultry

Maximum found

1500 1000 100 n.e.b 750 200

5 4 1 2 0 1

3 4 0 1 0 1

101 37 28 9 4 7

35 14 4 3 10 1

894.3 235.5 5LOQ 5LOQ 5LOQ 5LOQ

Notes: aMRLs – CODEX and EEC 2377/90. n.e., Non-established.

methods (Tables 7 and 8). Tables 4 and 5 shows the number of positive samples in the screening and confirmed by LC-MS/MS and UPLC-MS/MS in 2008 and 2009. In the confirmation of macrolides, pigs had a greater number of samples with residues. For AMGs, poultry had a larger number of samples with residues confirmed, especially neomycin and gentamycin. However, fewer positive samples were found in pigs for other antibiotics of the AMGs group (dihydrostreptomycin and spectinomycin) and one pig had a violation for dihydrostreptomycin (SDS) in 2008. These results confirmed that antibiotics have been used in both the poultry and pig industry since it is difficult to keep the breeding environment free from pathogens (Santos et al. 2003). Pig and poultry species had the greatest number of confirmations, which is consistent an increased frequency of drug use in these species. This result is explained because in pigs the development of large production systems resulted in an increasing challenge of diseases due to larger units, increased population density, production and handling. All these facts contributed to an increase in the use of antimicrobials in production to compensate for the situation (Burch et al. 2010). In poultry, antimicrobials are used as growth promoters for therapeutic

and prophylactic use (Lo¨hren et al. 2010). We can see groups of combinations of antibiotics, for example, lincomycin þ spectinomycin, lincomycin þ sulfadimidine, and lincomycin þ gentamicin, which can be administered orally or intramuscularly (Committee for Veterinary Medicinal Products 1998; Rutz and Lima 2001). In 2008, seven samples (0.5%) were positives for both macrolides and AMGs. In 2009, four samples (0.3%) were positive for both macrolides and AMGs, confirming the simultaneously use of these drugs. Studies in other countries have also shown antibiotic residues in poultry. Shareef et al. (2009) analysed 75 samples of stored poultry products selected from different markets in Ninevah city, Mosul, Iraq, for the presence of four antibiotics residues: oxytetracycline, sulfadiazine, neomycin and gentamycin. The results showed 39 positive samples (52%). From 25 samples of each tissue – liver, breast and thigh muscle – tested, seven (28%) of liver and breast muscle were positive for sulfadiazine and oxytetracycline, while seven (28%) of thigh muscle were positive for oxytetracycline and four (16%) samples of thigh muscle were positive for sulfadiazine. No neomycin or gentamycin residues were detected.

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C.K.V. Nonaka et al.

Table 8. Total samples analysed by the state in 2008 and 2009 by the Laboratory of Residues Veterinary Medicine (LRM/PL, MG, Brazil). Screening 2008

Screening 2009

State

Acre Bahia Distrito Federal Espı´ rito Santo Goia´s Minas Gerais Mato Grosso do Sul Mato Grosso Para´ Pernambuco Parana´ Roraima Rio Grande do Sul Santa Catarina Sa˜o Paulo Tocantins

0 4 0 0 2 9 11 0 0 0 15 2 15 2 1 2

3 2 0 5 66 46 101 110 12 0 25 22 4 11 49 6

0 0 0 0 17 44 27 22 0 0 68 0 128 153 9 0

0 1 8 0 38 19 26 13 0 8 135 1 86 87 42 0

0 0 10 0 0 7 12 0 1 0 0 0 4 0 20 2

9 3 0 0 12 51 46 67 105 12 0 1 40 26 16 14

0 0 0 0 0 9 33 26 20 0 0 0 75 0 127 192

0 0 3 8 0 37 34 25 23 0 1 18 96 0 81 111

Note: C, cattle; H, horse; P, poultry; S, swine.

According to the Annual Report on Surveillance for Veterinary Residues in Food in the UK (Veterinary Residues Committee (VRC) 2008a) and the 2008 FSIS National Residue Program Data of the USDA/FSIS (2008) data on the monitoring of antibiotic residues by screening and confirmation/quantification samples from domestic produce, violations found for some species/analytes were less than 1%. These documents emphasise the importance of the control of antibiotic residues in products of animal origin and the search for methodologies that include more classes of antibiotics used in production as well as being more sensitive. In 2008 in the United States (USDA/FSIS 2008) there were two violations of antibiotics: one for gentamycin and one for oxytetracycline. This publication also shows the results for other veterinary drug residues. In 2010 (USDA/FSIS 2010) the Residue Violation Information System published several violations of veterinary medicines from antibiotics: for neomycin, tilmicosin and sulfadimethoxine. The National Surveillance Scheme shows the results of the monitoring of residues for livestock in the UK and in 2008 ten violated samples for antibiotics were found: broiler muscle (n ¼ 1), turkey muscle (1), calf kidney (2), cattle kidney (1) and pig kidney (5). And in 2009 twelve violated were analysed for antibiotics: turkey muscle (n ¼ 2), broiler muscle (1), calf kidney (4) and cattle kidney (1) (VRC 2008b, 2009). Considering the origin of the samples and species monitored by the PNCRC, samples from 16 states of Brazil (Table 8) were analysed in 2008. The state of Santa Catarina was the one with the largest number of samples analysed (n ¼ 254), followed by Parana´ (243), Rio Grande do Sul (233), Mato Grosso do Sul (165),

Mato Grosso (145), Goia´s (123), Minas Gerais (118) and Sa˜o Paulo (101). In 2009 the samples came from 18 states (Table 6), with the inclusion of Amazonas and Paraı´ ba. Santa Catarina was the state with the largest number of samples analysed (n ¼ 319), followed by Rio Grande do Sul (244), Parana´ (215), Mato Grosso (149), Minas Gerais (125) and Mato Grosso do Sul (118). These states are those with a higher number of established producers, and regarding the species it can be observed that in the South there was a concentration of producers of swines and poultry and the Midwest a concentration of cattle. Each year the PNCRC seeks to include a larger number of establishments under Federal Inspection and attempt to monitor all Brazilian production.

Conclusions Screening is important because of the numbers of samples monitored by the PNCRC. Regarding the deadline for the release of results and the high cost of liquid chromatography, it would have been really hard to complete the monitoring only by the method of confirmation, since all the samples established by the macrolides and AMGs programme are analysed by the Laboratory of Residues Veterinary Medicine (LRM/PL). Results from the monitoring antibiotics residues in meat (cattle, pig, poultry and horse) for 2008/2009 showed that 99.97% of samples were in compliance with Brazilian legislation. Only one sample contained an antibiotic residue above the MRL. From these results we note that antibiotics are being used correctly

Food Additives and Contaminants and safely according to the veterinary practices recommended. The major antibiotics found were lincomycin, tilmicosin, tylosin and erythromycin (macrolides); and gentamycin, neomycin and dihydrostreptomycin (AMG), but always below their MRLs. Brazil, as the largest producer and exporter of some commodities, has a duty to ensure that its marketed products comply with the safety and quality standards demanded by consumers. The screening and confirmation/quantification methods that have been performed by the LRM are essential to provide valuable data for the assessment of the potential exposure set by the PNCRC and also to show the evolution of Brazilian monitoring and control of residues.

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Food Additives and Contaminants Vol. 29, No. 4, April 2012, 535–540

In-house validation of PremiÕ Test, a microbiological screening test with solvent extraction, for the detection of antimicrobial residues in poultry muscles C.G. Magalha˜esa*, C.R. De Paivaa, B.G. Botelhoa, A.M.G. De Oliveiraa, L.F. De Souzaa, C.V. Nonakaa, K.V. Santosb, L.M. Fariasc and M.A.R. Carvalhoc a Laborato´rio de Resı´duos de Medicamento Veterina´rios, Laborato´rio Nacional Agropecua´rio (LANAGRO), Pedro Leopoldo, Brazil; bUniversidade do Vale do Rio Doce (UNIVALE), Governador Valadares, Minas Gerais, Brazil; cDepartamento de Microbiologia, Instituto de Cieˆncias Biolo´gicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil

(Received 26 November 2010; final version received 31 August 2011) PremiÕ Test, a microbial inhibition test for the screening of antimicrobial residues, was validated according to the criteria established by Decision 2002/657/EC. Sensitivity, detection capability (CC ), specificity, selectivity, robustness and applicability were evaluated. The methodology involves the technique of solvent extraction, which increases the detection capability of the test for a wider range of antibiotics. The following CC values in poultry muscle were found: penicillin G 12.5 mg kg 1, total sulfonamides 75 mg kg 1, erythromycin 75 mg kg 1 and lincomycin 50 mg kg 1. The detection capability of chlortetracycline was equal to its maximum residue limit (100 mg kg 1) and the method did not detect gentamicin (1000 mg kg 1), for which no MRL is established in poultry muscle. Specificity evaluated in relation to different analytes and matrices did not detect any interferences in the tests results; whilst the robustness showed that the pH neutralisation point of the extract affects the analytical results and the kits’ performance. Only the screening of tetracyclines requires the analysis of extracts without pH neutralisation. The results of the validation process showed that this method is acceptable for screening -lactam, sulfonamide and macrolide antimicrobial groups in the National Residues and Contaminants Control Programme (PNCRC), and that for this it is fit for purpose. Keywords: animal products – meat; meat; veterinary drug residues – antimicrobials; veterinary drugs; method validation; microbiology; screening – microbial screening

Introduction Throughout history, infectious diseases have been a great threat to human and animal health and a prominent cause of morbidity and mortality. Today it is estimated that more than half of all antimicrobials produced globally are used in the treatment of animals. Antimicrobial agents can be administered to individual animals for treatment (therapy) or prevention (prophylaxis) of diseases. In animal production, antimicrobial agents can also be administered to healthy clinically animals, animals with clinical symptoms or to improve animal growth. To minimise the possible impact of antimicrobial use in animals on public and animal health, several international organisations such as the World Health Organization (WHO), the World Organization for Animal Health (OIE), the Food and Agriculture Organization of the United Nations (FAO), and the European Commission have in recent years emphasised the importance of a prudent and rational use of antimicrobials in animals (Guardabassi and Kruse 2010). Among the problems *Corresponding author. Email: cristina.magalhaes@yahoo.com.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 MAPA http://dx.doi.org/10.1080/19440049.2011.627571 http://www.tandfonline.com

related to drug residues in meat used as food include the development of bacterial resistance due to the transfer of resistance genes to human pathogens, the effect on the composition of the human intestinal microbiota, potential allergic reactions in sensitised individuals, direct toxicity and technological problems of fermented meat products (Popelka et al. 2005). Brazil, which has lush farming, needs to control antimicrobial residues in foods, particularly today when the practice is problematic in the context of international trade in animal food products (Brasil 1999). As a first step, the National Residues Control in Meat Programme (PNCRC) should include screening methods that can be defined, briefly, as qualitative or semi-quantitative tests that can detect the presence of residues in a species or matrices of interest, at a concentration below the maximum residue limit (MRL). A suspect result indicates that the MRL may have been exceeded and the sample should be analysed by a confirmatory quantitative method, providing the basis for regulatory action (Brasil 1999).

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Validation is the confirmation by examination and the provision of objective evidence that the particular requirements for a specific intended use are fulfilled (ISO 2005). For qualitative screening methods, according to Decision 2002/657/EC (European Commission 2002), the validation procedure establishes the following performance characteristics: detection capability (CC ), selectivity/specificity and applicability/robustness/stability. PremiÕ Test is a method for the screening of antimicrobial residues based on growth inhibition of Bacillus stearothermophilus, a thermophilic bacterium very sensitive to various antibiotics and sulfonamides (DSM Specialties 2009). Gaudin et al. (2008) concluded that PremiÕ Test could be used for the determination routine of antibiotics residues in muscle of different animal species with acceptable analytical performance. Solvent extraction provides an increase in the detection capability of the test for a wider range of drugs when compared with the physical fluid extraction (Stead et al. 2004). This method provides a stage after the screening, which allows classification into the groups of antimicrobial agents

-lactams, sulfonamides and tetracyclines. After screening, samples can be sent for chromatographic confirmation for these groups (Stead et al. 2004, 2007). Furthermore, in order to reduce the subjectivity of visual reading of the end-point associated with this microbial inhibition test, PremiÕ Test was coupled to scanner technology (Stead et al. 2005). In this work, the scanner technology was used in parallel with the visual readings to compare the results.

Materials and methods Chemicals and reagents PremiÕ Test kits were supplied by DSM Food Specialities R&D (Delft, the Netherlands). Lab Lemco broth was purchased from Oxoid (Basingstoke, UK). Penicillinase ( -lactamase 1,000,000 IU ml 1) was supplied by BD (Sparks, MD, USA). Para-aminobenzoic acid, sulfamethazine, sulfametoxipiridazine, sulfaquinoxaline, sulfadiazine, lincomycin, gentamicin and erythromycin analytical standards were purchased from Sigma-Aldrich (Poole, UK); sulfadimetoxine from Riedel de Ha¨en (Seelze, Germany); and sodium penicillin and chlortetracycline from Fluka (Poole, UK). Anhydrous sodium sulfate and methanol HPLC grade were supplied by J.T. Baker (Deventer, the Netherlands). Acetonitrile HPLC grade was supplied by Tedia (Fairfield, OH, USA); acetone analytical reagent grade was supplied by VETEC (Sa˜o Paulo, Brazil).

Solvent extraction The method used includes the methodology described in Department for Environment, Food and Rural Affairs (DEFRA), Central Science Laboratory (CSL) (2006): 10 ml of acetonitrile/acetone (70:30 v/v) were added to 5 g of anhydrous sodium sulphate to a 50 ml centrifuge tube containing 4 g of finely cut tissue. The extract was homogenised (30–40 s in the Ultra-Turrax), sonicated (5 min) and mixed by vortexing (30–40 s). The extract was then centrifuged (3900 rpm, 4 C, 15 min), the supernatant collected in a tube and it was reduced to about 200 ml under nitrogen (the bath water should not exceed 40 C). The volume was adjusted to 600 ml with Lab-Lemco broth (using a syringe and needle), sonicated (10 min) and mixed by vortexing (30–40 s). A modification was necessary in this method: pH neutralisation of the extract of analysed samples (pH 4.0–5.0) was adjusted to 7.0 using hydrochloric acid 0.25 mol l 1 and/or sodium hydroxide 0.25 mol l 1.

PremiÕ Test Aliquots of 100 ml of extract were dispensed in each vial of PremiÕ Test. The vials were incubated in a water bath (62–66 C) until the pH change of the blank sample (purple to yellow) when they were removed and the colour of the lower two-thirds of the agar observed. A yellow or yellow part (75%) indicates the absence of antimicrobials at or below the detection limit. A purple or purple in part (50–75%) indicates the possible presence of antimicrobials (Figure 1). The PremiÕ Scan results are expressed as a numerical value, Z. Positive values indicate positive results and negative values indicate negative results. The results of the automated readings were compared with those of visual readings in all experiments. In the case of different results between the visual and automated readings, the automated result was considered to be correct.

Figure 1. PremiÕ Test visual readings. Y – 100% yellow; YYP – 75% yellow/25% purple; YP – 50% yellow/50% purple; PPY – 75% purple/25% yellow; P – 100% purple.

Food Additives and Contaminants Post-screening The post screening for -lactam antibiotics and sulfonamides was carried out as described by Stead et al. (2004).

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kidney was first analysed by the Fast Antimicrobial Screen Test (FAST) (Dey et al. 2005). Muscle samples from the same set of samples of the kidneys that presented negative results in FAST were selected for this study.

Fortification procedure Analyte fortification was carried by applying a known concentration of drug to known blank tissue before extraction (Stead et al. 2004).

Evaluation of the sensitivity The detection limits obtained by Stead et al. (2004) were used as the initial concentration for the calculation of CC . Only the sensitivity of the macrolides was obtained experimentally, which was determined by the analysis of seven replicates of samples spiked with tylosin and tilmicosin at concentrations of 0.5, 0.75 and 1.0 MRL (MRL 100 and 75 mg kg 1 respectively), and for clindamycin, as there are no MRL established, the concentrations used were 0.5, 0.75 and 1.0 of the minimum level of proficiency of the confirmatory method for LC-MS/MS (100 mg kg 1) (US Department of Agriculture (USDA), Food Safety and Inspection Service (FSIS), Office of Public Health and Science 2003).

Determination of the detection capability To calculate the detection capability (CC ) of each antimicrobial agent examined, 21 samples were fortified at different concentration ranges, less than or equal to the MRL of each antibiotic. This experiment was repeated by increasing the concentration of the analyte until only one (or none) of 21 replicates presented a negative result. For qualitative screening methods, a rate of 5% for false-negative results is acceptable. CC was determined for the following antibiotics: penicillin G, sulfonamides pool (mix of sulfamethazine, sulfamethoxypyridazine, sulfadimethoxine, sulfadiazine and sulphaquinoxaline) and chlortetracycline. Furthermore, it was tested on erythromycin, gentamicin and lincomycin, antibiotics commonly found in the analysis of chromatographic confirmation by LC-MS/MS at levels below the MRLs in samples commonly analysed by the laboratory.

Evaluation of specificity In this study, the interference of two substances (nicarbazin and zearalenone), which may be present in the matrix, was evaluated. Five fortified matrix replicates were prepared at the concentration of 200 mg kg 1 for possible interferences, and five fortified matrix replicates at the concentration of 200 mg kg 1 for nicarbazin and zearalenone separately were added of penicillin G, sulfonamides pool and erythromycin in the respective MRLs. The specificity of the method was also evaluated in relation to different matrices, including bovine, swine and equine muscle. Five blank replicates of each of these matrices were prepared (without standard addition), as five fortified matrix replicates in the MRL concentration of penicillin G, sulfonamides pool and erythromycin.

Evaluation of the robustness This study evaluated the use of glass or plastic tubes and syringes washed and sterilised, the evaporation of the extract under a flow of air or nitrogen, and the pH of the extract (4.0â&#x20AC;&#x201C;5.0 and 7.0). Twelve replicate samples were submitted with every change, including three blank samples and three groups of fortified samples at the following concentrations: penicillin G 25 mg kg 1, sulfonamides pool 100 mg kg 1 and erythromycin 100 mg kg 1.

Evaluation of post-screening The evaluation of the post-screening step was developed by Stead et al. (2004). The integrated postscreening was assessed by adding 20 ml of -lactamase to five replicates of samples spiked with penicillin G and the addition of 50 ml of a solution of paraaminobenzoic acid (p-ABA 5 mg ml 1) to five replicates of samples spiked with the pool of sulfonamides. The samples were spiked at 1.0 and 2.0 MRL, while blank samples were added to distilled and deionised water.

Evaluation of selectivity

Results and discussion

To determine the selectivity of the test, 21 known blank poultry muscle samples were analysed. The sampling plan provided the receipt of samples of muscle, liver and kidney from the same animal for analysis. The

Sensitivity and detection capability The results for the detection capability obtained in this study are summarised in Table 1. For penicillin G and total sulfonamides, CC values corresponding to

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Table 1. CC determination for PremiÕ Test with solvent extraction.

Analytes Penicillin G (MRL ¼ 50mg kg 1) Sulfonamides pool (MRL ¼ 100mg kg 1) Chlortetracycline (MRL ¼ 100mg kg 1) Erythromycin (MRL ¼ 200mg kg 1) Lincomycin (MRL ¼ 100mg kg 1)

antimicrobials

Concentration (mg kg 1)

False-negative (%)

12.5

75 90 95 100 100 75 100 75

81 38 19 5 (CC ) 0 5 (CC ) 0 5 (CC )

0.25 and 0.75 of the MRL were found, respectively. A mixture of sulfonamides was spiked into one sample, therefore there is a cumulative effect of the five sulfonamides. Another work could analyse these compounds separately. Because these values were below the MRLs, more experiments with lower concentrations were not carried out. For erythromycin and lincomycin, 0.37 and 0.50 of the MRL were found, respectively. The CC determined for chlortetracycline was equal to the MRL of this analyte (1.0). The PNCRC determines the use of screening methods with a detection capability less than the MRL. The established criteria for validation of FAST were evaluated. This method has been used in the PNCRC for the screening of tetracyclines. With the exception of chlortetracycline, the method had a detection capability appropriate for the analytes studied. For gentamicin no MRL is established for poultry muscle. Negative results were obtained for 10 replicates of samples spiked with gentamicin at a concentration of 500 mg kg 1. In addition, 21 replicates of samples spiked with gentamicin at 1000 mg kg 1 were assessed, all also showing negative results. Tests to evaluate the sensitivity of the method to tylosin, tilmicosin and clindamycin showed positive results at the lowest concentration tested. Observing these considerations, samples fortified with penicillin G, a mixture of sulfonamides and erythromycin were used in the other experiments for validation. Over the past few years, various assessments of PremiÕ Test have been published based on samples of fortified meat broth (Reybroeck 2000a) or muscles of contaminated naturally poultry (Reybroeck 2000b). The sensitivity of PremiÕ Test was compared with other microbiological tests in several studies. However, these studies evaluated PremiÕ Test using the physical fluid extraction which is less sensitive than solvent extraction. PremiÕ Test can detect the presence of penicillin

residues below the MRL in poultry muscle samples (Popelka et al. 2003, 2005). Cantwell and O’Keeffe (2006) found that PremiÕ Test can detect in bovine kidney fluid all

-lactams (including cephalosporins) tested, as well lincomycin and doxycycline at the MRLs or below. This test could not detect other tetracyclines and sulfonamides below or equal to the MRLs and it was particularly insensitive to streptomycin and flumequine. According to these authors there is no ideal rapid microbial inhibition screening test applicable to all analytes. Schneider and Lehotay (2008) evaluated PremiÕ Test using penicillin G, sulfadimethoxine, oxytetracycline, tylosin, danofloxacin, streptomycin, neomycin and spectinomycin at a range of fortified concentrations in renal fluid and bovine serum. This method provides a characteristic profile of detectability. Gaudin et al. (2008) found that PremiÕ Test was more sensitive to -lactams and sulfonamides than one of the reference method tested. The detection capability of PremiÕ Test for -lactams (amoxicillin, ceftiofur), a macrolide (tylosin) and tetracycline was at the same level of their MRLs in samples of muscle, or even lower.

Selectivity/specificity In the selectivity study, two samples showed visual reading YP, but only one was positive in the scanner, resulting in an acceptable rate of 5% of false-positives. Nicarbazin is a coccidiostat indicated as an aid in the prevention of caecal and intestinal coccidiosis in poultry (Brasil 2000), but its presence at MRL concentration (200 mg kg 1) did not interfere in the responses of positive controls of the method, which showed clear positive responses. However, three of the samples containing only nicarbazin showed the visual readings YYP, with low negative values in the scanner. Zearalenone is an oestrogenic mycotoxin produced mainly by Fusarium graminearum, which can be found in raw material (grain) animal feed. Although zearalenone did not affect the performance of poultry in natural contamination, it should be noted that health authorities in some importing countries of poultry are on the alert for residues of zearalenone in meat as this mycotoxin in certain concentrations can induce an anabolic effect in humans and other mammals (Santurio 2000). Brazilian law does not set limits for zearalenone, but five countries around the world establish 200 mg kg 1 as the limit for this mycotoxin in corn and other cereals (FAO 2004). At this concentration, no interference of zearalenone was observed in the analysis. For the three species, the blank samples showed clear negative responses and the fortified samples gave

Food Additives and Contaminants clear positive responses. Thus, the method was suitable for the analysis of residues of -lactam, sulfonamide and macrolide antimicrobial groups in bovine, swine and equine muscle. Cantwell and O’Keeffe (2006) assessed the effect of the species cattle, pigs and sheep, which did not affect the response of PremiÕ Test. These authors evaluated the effect of species on the criteria of robustness.

Robustness The extraction step that involves sonicating and then mixing the extract in a vortex mixer has the objective of minimising the adsorption of analytes by glass tubes. However, the use of plastic or glass tubes showed no differences in the results of blank samples and fortified samples showed respectively negative and positive responses with the three analytes. The syringes used in the adjustment stage to 600 ml with broth are washed using chlorinated alkaline detergent (soaking for 3 h), rinsed thoroughly with tap water, and then with distilled and deionised water. In the validation experiments sterile syringes were used; this study had shown that there are no differences in the responses of blank and spiked samples when washed syringes are used, demonstrating the thorough decontamination of syringes. All blank samples showed negative responses and the fortified samples gave positive responses. The evaporation of the extract under a flow of air or nitrogen was also compared, since the use of compressed air was a more economical alternative for the use of nitrogen. The nitrogen cylinders are used in various tests in the laboratory must be continually replenished. Blank samples showed negative responses and the fortified samples gave positive responses. However, the samples spiked with erythromycin had fewer positive responses when the extracts were evaporated under a flow of compressed air. In addition, several tests were performed with a centrifugal evaporator, using a vacuum pump to evaporate the extract, but the time for evaporation to near dryness was too long (an average of 150 min), not leading to the release of the results in a single day, compared with the use of nitrogen or compressed air (an average of 75 min). The pH of the extract was evaluated by comparing the natural pH of the extract (4.0–5.0) and the extract with a pH close to neutrality (7.0) using solutions of NaOH 0.25 mol l 1 or HCl 0.25 mol l 1. The blank samples were negative in both conditions. Samples fortified with penicillin G were positive in both conditions, but more strongly positive with the neutralisation of the extract. Samples spiked with the pool of sulfonamides were negative in acidic pH, with visual reading YYP, but positive when the extract was neutralised. Finally, the samples spiked with

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erythromycin were negative in acidic pH, with the visual reading Y, but clearly positive when the extract was neutralised. Thus, the neutralisation of the pH of the extract appeared as a critical point of the analysis, constituting an essential step for optimal performance of the test.

Post-screening If a positive sample gives a negative response to treatment with -lactamase or p-ABA acid, it can be deduced that the observed antimicrobial residue consists of -lactams or sulfonamides, respectively. Blank samples showed negative results. Samples fortified with penicillin G showed negative results in reading visual (Y) and automated at two concentrations (50 and 100 mg kg 1), demonstrating inactivation of the antimicrobial activity of the analyte by -lactamase (an enzyme that hydrolyses the -lactam ring). Samples spiked with the mixture of sulfonamides at concentrations of 100 and 200 mg kg 1 showed negative results in the visual readings (Y and YYP, respectively) and automated, with Z-values of the samples fortified with 200 mg kg 1 corresponding to about half of the Z-values of the samples fortified with 100 mg kg 1. The mode of action of sulfonamides is the inhibition of diidropteroate synthetase in the synthesis of folic acid in the metabolism of prokaryotic cells. p-ABA is a natural agonist of the enzyme diidropteroate synthetase. The establishment of a competition for binding to the active site of the enzyme between sulfonamide and p-ABA enables the reversal of the bacteriostatic action of sulfonamides in the microbial cell. Using this technique, the selective inhibition of the effect of sulfonamides in the response of PremiÕ Test can be reached (Stead et al. 2004).

Applicability The microorganism employed in PremiÕ Test, B. stearothermophilus, showed greater sensitivity to selective antimicrobial compounds for Gram-positive compared with Gram-negative bacteria. For this reason, the sensitivity of the test to aminoglycosides compounds is low, whilst the technique shows sufficient sensitivity to the class of macrolides (Stead et al. 2004). Analysing the results, it was found that the CC of chlortetracycline was equal to the MRL of the analyte (100 mg kg 1), precluding the use of this method for the screening of the tetracycline class antibiotics because it minimises the possibility of releasing false-negative results. It was also verified that the detection of chlortetracycline is influenced by adjusting the pH of the extract, showing positive results only when the extract remains with the characteristic pH (4.0–5.0). For the screening of

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tetracyclines, it would require the analysis of ampoules added with extracts without pH adjustment, while the screening of -lactams, sulfonamides and macrolides is performed with the neutralisation of the extract. Thus, two vials should be used per sample for the screening of these groups, which would increase the cost of the analysis.

Conclusions PremiÕ Test is fast, easy to use and allows the analysis of a large number of samples simultaneously. The procedures required for validation of PremiÕ Test with solvent extraction showed that this method is sensitive, specific, selective and robust, in accordance with the criteria of Decision 2002/657/EC (European Commission 2002). Regarding applicability, PremiÕ Test proved suitable for the screening of residues of -lactams, sulfonamides and macrolides, and it may be used in the analysis of poultry, bovine, swine and equine muscle. The rates of false-negative and false-positive results were considered acceptable (5%), demonstrating that this method is applicable to the National Residues Control Programme (PNCR).

Acknowledgements The authors acknowledge Ma´rcio Teodoro Dias for his contribution in the review of this article.

References Brasil. Ministe´rio da Agricultura e do Abastecimento. 2000. Normas e padro˜es de nutriçaõ e alimentaçaõ animal; revisaõ. Brası´ lia (Brazil): MA/SARC/DFPA:152. Brasil. Ministe´rio da Agricultura, Pecua´ria e do Abastecimento. 1999. Instruçaõ normativa n 42 de 20 de dezembro de 1999. Plano Nacional de Controle de Resı´ duos em produtos de origem animal. Dia´rio Oficial da Uniaõ de 22 de dezembro de 1999. Brası´ lia (Brazil). Cantwell H, O’Keeffe M. 2006. Evaluation of the PremiÕ Test and comparison with the One-Plate Test for the detection of antimicrobials in kidney. Food Addit Contam. 23(2):120–125. Department for Environment, Food and Rural Affairs (DEFRA), Central Science Laboratory (CSL). 2006. Determination of antimicrobials at residue levels in food by the PremiTest microbiological test kit. SOP 37, Issue 3, 27 February 2006. Sand Hutton (UK). Dey BP, Thaker NH, Bright SA, Thaler AM. 2005. Fast antimicrobial screen test (FAST): improved screen test for detecting antimicrobial residues in meat tissue. J AOAC Int. 88:447–454. DSM Specialties. 2009. DSM Specialties – PremiÕ Test. [cited 2009 June 7]. Available from: http://www.dsm.com/ en_US/html/premitest/home.htm European Commission. 2002. Commission Decision 2002/ 657/EC, 2002, of 12 August 2002 implementing Council

Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off J Eur Commun. L 221:8–36. Food and Agriculture Organization of the United Nations (FAO). 2004. Worldwide regulations for mycotoxins in food and feed in 2003. FAO Food and Nutrition Paper No. 81. Rome (Italy): FAO. Gaudin V, Juhel-Gaugain M, More´tain J, Sanders P. 2008. AFNOR validation of PremiÕ Test, a microbiological-based screening tube-test for the detection of antimicrobial residues in animal muscle tissue. Food Addit Contam. 25(12):1451–1464. Guardabassi L, Kruse H. 2010. Principios da Utilizac¸a˜o Prudente e racional de antimicrobianos em animais. In: Guardabassi L, Jensen LB, Kruse H, editors. Guia de antimicrobianos em Veterina´ria. Porto Alegre (Brazil): ArtMed. ISO. 2005. General requirements for the competence of testing and calibration laboratories (ISO/IEC 17025:2005). Geneva: ISO. Popelka P, Nagy J, Germuska R, Marcincak S, Jevinova P, Rijk A. 2005. Comparison of various assays used for detection of beta-lactam antibiotics in poultry meat. Food Addit Contam. 22(6):557–562. Popelka P, Nagy J, Popelka PA, Marcincak S, Jevinova P, Hussein K. 2003. Comparison of BsDA and PremiÕ Test sensitivity to penicillin standards in poultry meat and after administration of Amuril plv.sol. Folia Veterinaria. 47:139–141. Reybroeck W. 2000a. Performance do PremiÕ Test using naturally contaminated meat. In: van Ginkel LA, Ruiter A, editors. Proceedings of the EuroResidue IV Conference, Veldhoven, the Netherlands, 2000. p. 909–912. Reybroeck W. 2000b. Detection of residues of antibiotics in foodstuffs with microbiological tests using bacillus. In: Proceedings of the Bacillus Symposium, Bruges, Belgium, 30–31 August 2000. Santurio JM. 2000. Micotoxinas e micotoxicoses na avicultura. Revista Brasileira de Cieˆncia Avı´ cola. 2:1–12. Schneider MJ, Lehotay SJ. 2008. A comparison of the FAST, PremiÕ and KISTM tests for screening antibiotic residues in beef kidney juice and serum. Anal Bioanal Chem. 390:1775–1779. Stead S, Richmond S, Sharman M, Stark J, Geijp E. 2005. A new approach for detection of antimicrobial drugs in food PremiÕ Test coupled to scanner technology. Analyt Chim Acta. 529:83–88. Stead S, Sharman M, Tarbin JA, Gibson E, Richmond S, Stark J, Geijp E. 2004. Meeting maximum residue limits: an improved screening technique for the rapid detection of antimicrobial residues in animal food products. Food Addit Contam. 21:216–221. Stead SL, Caldow M, Sharma A, Ashwin HM, Sharman M, De-Rijk A, Stark J. 2007. New method for the rapid identification of tetracycline residues in foods of animal origin – using the PremiÕ Test in combination with a metal ion chelation assay. Food Addit Contam. 24(6):583–589. US Department of Agriculture (USDA), Food Safety and Inspection Service (FSIS), Office of Public Health and Science. 2003. Confirmation of macrolide/lincosamide antibiotics by ion trap HPCL/MS/MS. SOP No. CLG-565 MAL1.00, 14 November 2003:19. Washington (USA).

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 541–549

Optimisation and validation of a quantitative and confirmatory LC-MS method for multi-residue analyses of b-lactam and tetracycline antibiotics in bovine muscle C.P. Rezendea*, M.P. Almeidaa, R.B. Britoa, C.K. Nonakab and M.O. Leitec a

Ministry of Agriculture, Livestock and Food Supply, MAPA, National Agricultural Laboratory, LANAGRO/MG, Brazil; Bolsista CNPq, National Agricultural Laboratory, LANAGRO/MG, Brazil; cVeterinary School of Federal University of Minas Gerais – UFMG, Belo Horizonte/MG, 31270-901, Brazil b

(Received 23 November 2010; final version received 31 August 2011) A multi-residue method for the determination of the -lactam antibiotics ampicillin, cefazolin, cloxacillin, dicloxacillin, nafcillin, oxacillin, penicillin G, penicillin V and the tetracyclines chlotetracycline, tetracycline and oxytetracycline was optimised and validated in bovine muscle. The method is based on the extraction of the residues from muscle using water/acetonitrile (2/8, v/v) with subsequent use of dispersive solid-phase C18 and hexane for purification. Extracts were analysed using ultra-performance liquid chromatography (UPLC-MS/MS) coupled with the mass spectrometer in positive electrospray ionisation mode (ESIþ) for all analytes. The method was validated according to the requirements of European Commission Decision 2002/657/EC. The validation results were obtained within the MRL range of 0–1.5 of the MRL, with recoveries varying from 90% to 110% and CV 5 20% (n ¼ 54), except for cloxacillin, dicloxacillin and nafcillin. However, matrix interference was observed. The decision limit (CC ) ranged from 10% to 15% of the MRL. The uncertainty measurement was estimated based on both bottom-up and top-down strategies and the uncertainty values were found to be lower than 20% of the MRL. The method has a simple extraction procedure whereby analytes are separated with reasonable resolutions in a single 11-min chromatographic run. According to the validation results, this method is suitable for monitoring -lactams and tetracyclines according to National Program for Residue and Contaminant Control – Brazil (NPRC-Brazil) in bovine muscle. Keywords: animal products – meat; veterinary drug residues – antibiotics; chromatography – LC/MS

Introduction Antibiotics of -lactam classes (penicillin and cephalosporins) and tetracyclines have been widely used in animal production as chemotherapeutic growth promoters and as prophylactics to prevent and treat infectious diseases such as mastitis and pneumonia. The widespread use of these drugs can lead to the presence of residues in food products of animal origin, which may have adverse effects on consumer health, including bacterial resistance to these drugs in humans and also potential risk for individuals who are hypersensitive to them (Grunwald and Petz 2003; Riediker et al. 2004; Msagati and Nindi 2007; Bailo´n-Pe´rez et al. 2009; Holthoon et al. 2010). The penicillin group is composed of substances that contain a thiazolidinic ring connected to a -lactam ring and a lateral amino chain (Figure 1a). Radical acids can be linked to this lateral chain (which can be cleaved by various amidases, including bacterial ones). The structural integrity of the nucleus of 6-aminopenicillanic acid is crucial to theses drugs’ activities. The link of different radicals to the amino group of

6-aminopenicillanic acid determines the essential pharmacological activities of the resulting molecules (Hammel et al. 2008; Bogialli and Corcia 2009). The tetracyclines comprise a group of antibiotics that are used therapeutically and as prophylactics in animals against Gram (þ) and ( ) microorganisms, to treat infectious diseases, and as additives in food products (Castellari et al. 2009). The antibiotics most widely used are tetracycline, oxytetracycline, chlortetracycline and doxycycline, which chemical structure is represented in Figure 1(b). To avoid health risks for the costumer due to residues, regulatory agencies have defined maximum residue limits (MRL) for pharmacologically active substances in various food products. The limits implemented in Commission Regulation EU 37/2010 (European Commission 2009) are shown in Table 1. To monitor the possible occurrence of residues in food products for human consumption, there is the need for implementation of sensitive analytical methodology (US Department of Agriculture, Food Safety and Inspection Service, Office of Public Health Science 2007) to determine these compounds at very

*Corresponding author. Email: cristiana.rezende@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 MAPA – Brazil http://dx.doi.org/10.1080/19440049.2011.627883 http://www.tandfonline.com

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Figure 1. General structure of penicillins (a) and tetracyclines (b).

Table 1. Maximum residue limits (MRL) for penicillin and tetracycline in muscle. Species

MRL (mg kg 1)

All All All All All Porcine/poultry All All All All All

50 50 300 300 300 25 n.e. 300 100 100 100

Residue Ampicillin Penicillin G Oxacillin Cloxacillin Dicloxacillin Penicillin V Cefazolin Nafcillin Tetracycline Oxytetracycline Chlortetracycline

Note: n.e., Not established. Source: European Commission (2009).

low concentrations. This study is about the optimisation and validation of a quantitative and confirmatory multi-residue method for analysis of 11 -lactam and tetracycline antibiotics in bovine muscle. The method is based on the extraction of the residues from the muscle with the water/acetonitrile with subsequent clean-up with dispersive solid-phase C18 and hexane. The extracts were analysed by ultra-performance liquid chromatography coupled with mass spectrometric detection (UPLC-MS/MS).

Materials and methods Chemicals and reagents Reference standard of ampicillin (98.6%), cloxacillin (97.1%), dicloxacillin (99.8%), nafcillin (94.1%) oxacillin (95.0%), penicillin G (99.1%), penicillin V (99.0%), deuterated penicillin G, chlortetracycline (90.0%), tetracycline (97.7%), oxytetracycline (99.1%) and cefazolin (99.4%) were purchased from Sigma Aldrich (St Louis, MO, USA). Acetonitrile, methanol and hexane were of HPLC-grade from Merck (Germany); water was generated by a Milli-Q purification system; formic acid was purchased from Fluka (USA).

Standard solutions Standard solutions were prepared at 200 mg ml 1, the

-lactam standards were diluted in water/acetonitrile and the tetracycline in methanol. These solutions were named stock solutions. All solutions were stored for 1 month in a freezer at temperature lower than 12 C, monitored throughout the validation. The stock solutions were diluted to prepare a pool of all analytes at concentrations of 0.25 mg ml 1 for penicillin V, 0.5 mg ml 1 for ampicillin and penicillin G, 3.0 mg ml 1 for cloxacillin, dicloxacillin, oxacillin, cefazolin and nafcillin, and 2.0 mg ml 1 for tetracycline, oxytetracycline and chlortetracycline. This solution was named a work solution and was stored in a freezer at less than 12 C for up to 1 week. Penicillin G prepared at a concentration of 100 mg ml 1 was used as a deuterated internal standard.

Samples Samples of bovine muscle that did not contain any of the studied analytes were used as blank samples.

Samples preparation A sample of 2.00 0.10 g of bovine muscle was weighed in a 50 ml polypropylene tube and fortified with the work solution and the internal standard 1.0 mg ml 1 solution according to the validation step: linearity range, matrix effect, trueness, precision, CC , CC and selectivity. Deionised water (2 ml) and acetonitrile (8 ml) were added, followed by homogenisation in an Ultra Turrax blender. The samples were centrifuged at 1100 g for 20 min at 10 C and 5 ml of hexane were added to the extract to remove excess fat. The hexane was removed and 0.50 g of the C-18 dispersive phase was added to the aqueous phase. The mixture was agitated and centrifuged and the supernatant evaporated under compressed air flux or nitrogen at 40 C ( 5 C) to a final volume of approximately 1.5 ml. The concentrated extract had its volume adjusted to 2 ml with distilled and deionised water and then filtered through a 0.45 -mm PTFE membrane and injected into the UPLC/MS/MS.

Food Additives and Contaminants Table 2. Gradient of mobile phase.

Time (min) 0.00 1.00 5.00 5.50 9.00 9.10 11.00

%A: water Ăž0.1% formic acid

%B: MeOH

95.0 95.0 50.0 5.0 5.0 95.0 95.0

5.0 5.0 50.0 95.0 95.0 5.0 5.0

543

decision limit (CC ), detection capability (CC ) and selectivity.

Equivalent of analytes on blank matrix The verification of possible interferences and the equivalent of the analytes in the matrix of bovine muscle was performed with analysis of 12 blank samples.

Selectivity LC-MS/MS analysis For detection and quantification of analytes, an ACQUITY HPLC system, coupled to a Waters Quattro Premier XE triple quadruple, was used. The mass spectrometer parameters were optimised by direct infusion of the standards using a mobile phase of 50% phase A (water Ăž0.1% formic acid) and 50% phase B (methanol). The chromatographic conditions were optimised from the injection of direct standard solutions. Tests such as flux and gradient of mobile phase, column temperature, and volume of injection were performed. Five different columns were tested for this method: Column Agilent Eclipse HPLC XDB C18, 5.0 mm, 4.6 150 mm, Column Agilent Zorbax Eclipse UPLC XDB-C18, 1.8 mm, 4.6 50 mm, Column Agilent Zorbax SB-C18 3.5 mm, 2.1 100 mm, Phenomenex Luna C18 150 3.0 mm and Column Waters Acquity UPLC BECH C18, 1.7 mm; 2.1 50 mm. The best chromatographic separation was obtained using Column Waters Acquity UPLC BECH C18, 1.7 mm; 2.1 50 mm and pre-column Van Guard Waters BECH C18, 1.7 mm at 40 C, using water with 0.1% formic acid and methanol as mobile phase at a flow rate of 0.60 ml min 1. The gradient of the mobile phase is shown in Table 2. The total running time was 11 min and the injection volume was 20 ml. A chromatogram obtained from a recovery containing penicillin, cephalosporins and tetracycline is shown in Figure 2. Detection was carried out using electrospray ionisation in positive mode with a capillary voltage of 3.5 kV, source temperature of 120 C, de-solvation temperature of 400 C, and cone and de-solvation gas flow rates of 1 and 2 L h 1, respectively. The transitions monitored and collision energies used are detailed in Table 3.

Method validation The validation of this method followed the requirements of European Commission Decision 657/2002/ EC. The main parameters evaluated were linearity and working range, matrix effect, accuracy, precision,

Selectivity was evaluated by adding possible interfering compounds to the samples to test for inhibition or improvement in the detection or quantification of

-lactam and tetracycline in the presence of the same (Souza 2007). Six replicates were analysed at levels of 0.5, 1.0 and 1.5 MRL totalling 18 samples. Besides the studied analytes, antibiotics of the macrolide group and lincomycin at three replicates at each level were added. Tilmicosin, clindamicyn, eritromicym, tylosin and lincomicyn at concentrations 1000, 200, 200, 1500 and 1500 mg kg 1 respectively were among the added analytes.

Linearity and working range The linearity and range of work were determined with the injection, in triplicate, of standard solutions at concentrations 0.50, 0.75, 1.00, 1.25 and 1.50 MRL, including a blank sample, over three different days.

Matrix effects Matrix effects were studied by a comparison between the direct standard curves and fortified extract curve of the blank matrix. Five blank samples were extracted and after evaporation the extracts were fortified with standard at concentrations 0.50, 0.75, 1.00, 1.25 and 1.50 MRL. A standard curve in pure solvent was also prepared at the same concentrations of the matrix extract. This experiment was performed on three different occasions.

Trueness Trueness studies, repeatability and intra-laboratory reproducibility were obtained by two different analysts that individually repeated the extraction at three concentration levels (0.5, 1.0 and 1.5 MRL) giving six replicates by level, resulting in 18 extractions per day, over three different days. The total number of samples extracted per analyst was 54. The quantification of all samples analysed in the experiment to study the

Figure 2. Chromatogram of penicillins, cephalosporins and tetracyclines obtained from a recovery containing penicillin and tetracycline at concentrations: 25 mg kg 1 penicillin V, 50 mg kg 1 ampicillin and penicillin G, 300 mg kg 1 cloxacillin, dicloxacillin, nafcillin, oxacillin and cefazolin, 100 mg kg 1 chlortetracycline, tetracycline and oxytetracycline.

544 C.P. Rezende et al.

545

Figure 2. Continued.

Food Additives and Contaminants

546

C.P. Rezende et al.

Table 3. Retention time, base ion, monitored transitions, cone and energy of collision at the validation of multi residues of

-lactam and tetracyline. Retention time (min)

Precursor ion (m/z)

MRM transition (m/z)

Cone voltage (V)

Collision energy (eV)

Ampicillin

3.29

350.1

Cefazolin

3.27

455.1

Cloxacillin

5.66

436.1

Dicloxacillin

5.73

470.1

Nafcillin

5.73

415.0

Oxacillin

5.60

402.1

Penicillin V

5.58

351.1

Penicillin G

5.17

335.2

Chlortetracycline

3.86

479.1

Oxytetracycline

3.08

461.1

Tetracycline

3.00

445.1

Penicillin G deuterated

5.13

342.0

79 106 156 323 114 160 114 160 199 256 114 160 114 160 114 160 98.1 444.1 98 426 98.1 392.1 160.0 182.5

50 25 15 10 40 20 45 15 20 24 35 10 35 15 35 10 35 25 45 20 40 30 13 13

Compounds

repeatability were obtained using fortified blank matrix (recovered) calibration curve.

Precision The evaluation of precision was performed from the relative standard deviations (RSD %) obtained from the analysis performed in the recovery trials under repeatability conditions and intra-laboratory reproducibility (intermediate precision). For repeatability the results of analyst 1 (n ¼ 54) were considered, whereas the intermediate precision was determined through the combination of the results of the two analysts (n ¼ 108). Decision limit (CC ) and detection capability (CC ) These parameters were obtained through the analysis of one blank sample and five fortified blank samples at concentration levels of 0.5, 0.75, 1.00, 1.25 and 1.50 MRL. The samples were injected in triplicate and this experiment was repeated on two other occasions.

Estimate of measurement of uncertainty The measurement uncertainty estimate was obtained from the combination of uncertainties of the

20 20 20 17 20 18 20 25 25 25 16

calibration curve and intra-laboratory reproducibility using a ‘top-down’ methodology and for this study was it not regarded as including the uncertainty of sampling. The uncertainty of the calibration takes into account the uncertainty of the intercept and slope, as the uncertainty of reproducibility is determined from the RSD under these conditions.

Results and discussion Method validation The extraction procedure used in this method is relatively fast and simple when compared with other methods, since it uses dispersive-SPE for clean-up. The traditional methods that determine -lactam and tetracycline mostly use an SPE cartridge (Becker et al. 2004; Chico et al. 2008) that requires more care in controlling solvent flow, cartridge drying and other parameters. The ions that characterise the analytes as well as their relative intensity comply with Commission Decision 2002/657/EC (Table 4). The signal-to-noise ratio obtained was higher than 3 for all analytes. The blank samples did not show any interference of the analyte and the intensity was zero or relatively low compared to the samples with added standard at the lowest level calibration.

Food Additives and Contaminants Table 4. Relative intensity of the ions.

Analyte

Media

Deviation (%)

Parameters (European Commission 2002) (%)

Ampicillin Cefazolin Cloxacillin Dicloxacillin Nafcillin Oxacillin Penicillin G Penicillin V Chlortetracycline Oxytetracycline Tetracycline Penicillin G deuterated

14.8 60.0 38.1 33.1 9.2 35.1 35.7 33.5 55.2 5.81 4.6 96.8

22.8 4.9 6.8 8.3 7.2 8.74 13.5 13.4 13.1 19.0 13.0 4.6

30 20 25 25 50 25 25 25 20 50 50 20

547

a t-test for a comparison of the obtained average at trials with and without the matrix for each level of concentration studied. The curves were compared on 3 distinct occasions (days) with point-to-point mean comparisons of the analyte responses in the matrix-matched fortified samples and standard solutions at the same range of concentrations of the calibration curves obtained in the same analysis. The results show there is a matrix effect between the direct standard curve and the fortified samples curve. Due to the significant result of the matrix effect, the samples were quantified using a matrix curve in all experiments and showed satisfactory linearity.

Trueness

Selectivity To assess selectivity the average recoveries were compared by a Studentâ&#x20AC;&#x2122;s t-test at a significance of 95%. The results showed there was no expressive statistical difference among the average recoveries of

-lactams and tetracyclines in the samples to which the macrolide was added when compared with the samples to which there was no addition.

Linearity and working range The linearity of the chromatographic response was tested with spiked calibration curves using six calibration points at concentrations ranging from zero to 1.50 MRLs. The homogeneous (homoskedastic) or heterogeneous (heteroskedastic) scatters across the concentration range of the curves were considered in the curve fitting as well as in the regression coefficients (r2). The linear range for all analytes is from zero to 1.50 MRL and the r2 for all the used calibration curves were 0.93. It was also observed that there were different standard deviations at different concentration levels (heteroskedasticity), leading to the conclusion of a weighted linear regression model for the spiked calibration curves. Higher concentration levels were not verified because 2002/657/EC recommends 1.5 MRL as the highest concentration level, as confirmed by Fagerquist et al. (2005). At concentrations higher than 450 mg kg 1 the variables do not adjust linearly for ampicillin, cefazolin, cloxacillin, dicloxacillin, nafcillin, oxacillin, penicillin G, and penicillin V using LC-MS/MS for detection.

Matrix effects The study of the matrix effect involved using an F-test for the evaluation of the homoskedasticity of data and

Trueness of the method was evaluated by recovery of the analytes added to blank samples and submitted to the extraction procedure. The recovery values for

-lactams and tetracyclines calculated for fortified blank matrix varied between 90% and 110% (Table 5) and were considered satisfactory according to Commission Decision 2002/657/EC, which stipulated recoveries of 70â&#x20AC;&#x201C;120%.

Precision The precision evaluation of the method occurred from the RSDs of intra-laboratory repeatability and reproducibility (Table 5). The values were lower than those suggested by Commission Decision 2002/657/EC for all analytes except for cloxacillin, dicloxacillin and nafcillin that are underlined in Table 5.

Decision limit (CC ) and detection capability (CC ) CC and CC determined according to EC 2002/657/ EC were calculated through experiments with calibration curves. The values obtained are shown Table 6.

Measurement uncertainty The measurement uncertainty estimate was obtained from the combination of uncertainties of the calibration curve and intra-laboratory reproducibility, following recommended by the top-down methodology, and for this study is not regarded to include the uncertainty of sampling. The uncertainty of the calibration takes into account the uncertainties of the intercept and slope, while uncertainty of reproducibility is determined by the RSD under these conditions. The calibration curve for uncertainty and the intermediate precision were calculated and used to obtain the combined measurement uncertainty and

548

C.P. Rezende et al. Table 5. Recovery values and RSD of the method obtained under repeatability conditions (n Âź 54) and reproducibility (n Âź 108).

Analytes Ampicillin Cefazolin Cloxacillin Dicloxacillin Nafcillin Oxacillin Penicillin G Penicillin V Chlortetracycline Oxytetracycline Tetracycline

Concentration (mg kg 1)

Recorded (%)

RSD reproducibility

RSD repeatability

25 50 75 150 300 450 150 300 450 150 300 450 150 300 450 150 300 450 25 50 75 12.5 25 37.5 50 100 150 50 100 150 50 100 150

86.1 100.4 104.6 102.7 104.9 112.3 95.9 99.5 91.2 89.1 93 92.9 98 103.9 94.6 97.2 97.9 92.5 97.9 101.7 100.6 98.3 100.3 92.8 95.5 101.8 98.2 98.2 102.2 100.2 97.6 99.4 95.3

16.5

17.3

10.6

11.7

20.0

17.4

37.5

26.5

28.6

25.6

15.3

13.5

7.5

6.3

17.1

15.1

10.9

Table 6. CC , CC and the estimate of uncertainty of measurement determined at the validation procedure on bovine muscle.

Analytes

Estimate of measurement MRL CC CC

uncertainty (mg kg 1) (mg kg 1) (mg kg 1) (mg kg 1)

8.8

8.5

10.3

9.2

multiplied by a factor of 2 (k) to obtain the expanded uncertainty. The expanded uncertainties for the

-lactams and tetracyclines at the MRL level are shown in Table 6.

Conclusions Ampicillin Cefazolin Cloxacillin Dicloxacillin Nafcillin Oxacillin Penicillin G Penicillin V Chlortetracycline Oxytetracycline Tetracycline

50 300 300 300 300 300 50 25 100 100 100

56.1 336.5 348.5 359.1 371.7 356.5 55.7 30.9 111.2 109.8 110.3

62.2 373.1 397.1 418.1 443.4 413.1 61.5 36.8 122.4 119.6 120.5

8.4 38.1 50.3 54.5 65.0 74.0 8.2 8.2 20.4 17.7 18.7

The proposed method offers a simple analysis separating the analytes at good resolution in a single chromatographic running of 11 min. The detection/ quantification using UPLC-MS/MS has been demonstrated to be a sensitive technique for multi-residue determination of -lactams: ampicillin, cefazolin, oxacillin, penicillin G, penicillin V, and tetracyclines: chlortetracycline, tetracycline and oxytetracycline in samples of bovine muscle. The method performance parameters indicated adequacy for the use of the method for monitoring in the Brazilian programme for

-lactam and tetracycline residues in samples of bovine

Food Additives and Contaminants muscle. All validation stages were in conformation to Decision 2002/657/EC (European Commission 2002).

References Bailo´n-Pe´rez MI, Garcı´ a-Campan˜a AM, Olmo-Iruela M, Ga´miz-Gracia L, Cruces-Blanco C. 2009. Trace determination of 10 -lactam antibiotics in environmental and food samples by capillary liquid chromatography. J Chromatogr A 1216:8355–8361. Becker M, Zittlau E, Petz M. 2004. Residue analysis of 15 penicillins and cephaloporins in bovine muscle, kidney and milk by liquid chromatography-tandem mass spectrometry. Analyt Chim Acta 520:19–32. Bogialli S, Corcia AD. 2009. Recent applications of liquid chromatography-mass spectrometry to residue analysis of antimicrobials in food of animal origin. Analyt Bioanalyt Chem. 395:947–966. Castellari M, Grataco´s-Cubarsı´ M, Garcı´ a-Regueriro JA´. 2009. Detection of tetracycline and oxyteracycline residues in pig and calf hair by ultra-high-performance liquid chromatography tandem mass spectrometry. J Chromatogr A 1216:8096–8100. Chico J, Meca S, Companyo´ R, Prat MD, Granados M. 2008. Restricted access materials for sample clean-up in the analysis of trace levels of tetracyclines by liquid chromatography – application to food and environmental analysis. J Chromatogr A 1181:1–8. European Commission. 2002. Commission Decision 2002/ 657/EC of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Brussels (Belgium): European Commission.

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Fagerquist CK, Lightfield AR, Lehotay SJ. 2005. Confirmatory and quantitative analysis of -lactam antibiotic in bovine kidney tissue by dispersive solid-phase extraction and liquid chromatographytandem mass spectrometry. Analyt. Chem. 77:1473–1482. Grunwald L, Petz M. 2003. Food processing effects on residues: penicillins in milk and yoghurt. Analyt Chim Acta 483:73–79. Hammel YA, Mohamed R, Gremaud E, LeBreton MH, Guy PA. 2008. Multi-screening approach to monitor and quantify 42 antibiotic residue in honey by liquid chromatography-tandem mass spectrometry. J Chromatogr A 1177:58–76. Holthoon FV, Mulder PPJ, Bennekom EOV, Heskamp H, Zuidema T, Rhijn HJAV. 2010. Quantitative analysis of penicillins in porcine tissues, milk and animal feed using derivatisation with piperidine and stable isotope dilution liquid chromatography tandem mass spectrometry. Analyt Bioanalyt Chem. 396:3027–3040. Msagati TAM, Nindi MM. 2007. Determination of

-lactam residues in food stuffs of animal origin using supported liquid membrane extraction and liquid chromatography-mass spectrometry. Food Chem. 100:836–844. Riediker S, Rytz A, Stadler RH. 2004. Cold-temperature stability of five -lactam antibiotics in bovine milk and milk extracts prepared for liquid chromatographyelectrospray ionization tandem mass spectrometry analysis. J Chromatogr A 1054:359–363. Souza SVC. 2007. Procedimento para validac¸a˜o intralaboratorial de me´todos de ensaio: delineamento e aplicabilidade em ana´lise de alimentos. Tese de doutorado. Belo Horizonte/MG, Brazil.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 550–558

Determination and confirmation of chloramphenicol in honey, fish and prawns by liquid chromatography–tandem mass spectrometry with minimum sample preparation: validation according to 2002/657/EC Directive Fabiano Barretoab*, Cristina Ribeiroa, Rodrigo Barcellos Hoffac and Teresa Dalla Costab a

Ministe´rio da Agricultura, Pecua´ria e Abastecimento, Laborato´rio Nacional Agropecua´rio – LANAGRO/RS, Porto Alegre, RS, Brazil; bPrograma de Po´s-Graduaçaõ em Cieˆncias Farmaceûticas, Faculdade de Farmaćia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; cInstituto de Quı´mica, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil (Received 28 November 2010; final version received 8 November 2011) A reliable, simple and sensitive liquid chromatography–electrospray ionisation-tandem mass spectrometry (LC–ESI-MS/MS) confirmation method has been developed for chloramphenicol (CAP) determination in honey, fish and prawns. For honey, samples were extracted with ethyl acetate, an aliquot was evaporated to dryness and re-dissolved in mobile phase. For fish and prawns, tissues were extracted with acetonitrile and chloroform. The organic layer was evaporated to dryness and the residue was re-constituted with water: acetonitrile (90:10). LC separation was achieved on a C18 column with gradient elution using a mobile phase of acetonitrile and water. Analysis was carried out on a triple–quadrupole tandem mass spectrometer in multiple reaction monitoring (MRM) mode via electrospray interface operated in negative ionisation mode, with deuterated chloramphenicol-d5 (d5-CAP) as internal standard. Method validation was performed according to the criteria of Commission Decision 2002/657/EC. Four identification points were obtained for CAP with one precursor ion and two product ions. The limit of detection (LOD) was 0.02 mg kg 1. Linear calibration curves were obtained over concentration ranges of 0.1–1.0 mg kg 1 in tissues. Mean recoveries ranged from 85.5% to 115.6%, with the corresponding intra- and inter-day variation ranging from 1.0% to 22.5%, depending on matrix type and level of concentration. The decision limit (CC ) and detection capability (CC ) of the method were obtained for all matrices: 0.04 and 0.06 mg kg 1, respectively, for prawns and fish and 0.05 and 0.09 mg kg 1 for honey. Keywords: chromatographic analysis; LC/MS; clean-up; chloramphenicol; meat; fish and fish products; honey

Introduction Chloramphenicol (CAP) is a broad-spectrum antibiotic isolated from Streptomyces venezuelae and industrially obtained by chemical synthesis. CAP belongs to the amphenicols drug family, which has been widely used in veterinary medicine for treatments of various infections. CAP is effective against a wide range of microorganisms, including most gram-positive and gram-negative bacteria, Rickettsia, Chlamydia, some Mycoplasma species and all anaerobes. Despite all that, CAP can cause fatal bone marrow depression (aplastic anemia) in 1:25,000 to 1:40,000 humans. The effect is not dose-related. It can also cause a dose-related reversible anemia in humans because of inhibition of mitochondrial protein synthesis in mammalian bone marrow cells. Because of these toxic side effects on haemopoietic system (Marsh et al. 2009) and emergence of drug-resistant bacteria (Phillips 2008; Ali et al. 2009), their clinical applications are strictly controlled in many countries including Brazil, China, United

States and member states of the European Union (EU) (Mauricio et al. 2009; Rejtharova and Rejthar 2009). CAP has been banned for use in food-producing animals in the EU and a minimum required performance limit (MRPL) for analytical methods has also been set for CAP (0.3 mg kg 1) in foodstuffs of animal origin (European Commission 2002, 2010). Similarly in 2003, Brazil had also adopted the same position and banned the use of CAP in food-producing animals (Brasil 2003). Therefore, it is of great importance to develop sensitive methods for the determination and confirmation of CAP in animal tissues. Many different analytical methods have been developed for amphenicols determination in animal tissues, such as gas chromatography (GC) (Shen et al. 2009; Silva et al. 2010), liquid chromatography (LC) (Ali et al. 2009; Aresta et al. 2009), GC–mass spectrometry (MS) (Shen et al. 2009), LC–MS and LC–MS/ MS (Rodziewicz and Zawadzka 2008; Rejtharova and Rejthar 2009; Shen et al. 2009), capillary

*Corresponding author. Email: fabiano.barreto@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.641160 http://www.tandfonline.com

Food Additives and Contaminants electrophoresis (Zhang, Wang, et al. 2008) and other methods (Huang et al. 2009). For CAP extraction and concentration, several techniques had been applied, as solid-phase extraction, molecularly imprinted polymers, biosensors and others (Guo et al. 2008; Rodziewicz and Zawadzka 2008; Wihlborg et al. 2008). However, validation procedures according to Commission Decision 2002/657/EC were reported only for a few methods. This regulation established four identification points for the confirmation of the CAP (Nicolich et al. 2006; Ro¨nning et al. 2006). Therefore, it is essential to establish a reliable and sensitive LC–MS/ MS method that could meet the confirmatory criteria of the 2002/657/EC Decision. Furthermore, honey is a well-known problematic matrix for residues analysis and reported methods for CAP analysis in this matrix besides prawns and fish usually applied solid-phase extraction (SPE) procedures to clean up samples. The aim of the present study was to develop and validate a confirmative LC–MS/MS method for the determination of CAP in honey, fish and prawns according to the criteria of the 2002/657/EC Decision and using minimum sample preparation steps. In this method, samples were prepared with liquid–liquid extraction and the LC–MS/MS analysis was carried out in negative electrospray ionisation mode.

Materials and methods Reagents and materials CAP (99.0%) and thiamphenicol (TAP, 99.0%) standards were obtained from Sigma-Aldrich (St. Louis, Missouri, USA). Deuterated chloramphenicol-d5 (d5-CAP, 98.0%) used as internal standard was obtained from Cambridge Isotope Laboratories (Andover, Massachusetts, USA). Methanol, acetonitrile, ethyl acetate and chloroform of high-performance liquid chromatography (HPLC) grade were from J.T. Baker (Phillipsburg, NJ, USA). HPLC-grade water was obtained from Milli-Q purification unit (Millipore, Bedford, Massachusetts, USA).

551

6 months at 20 C. Working standard solutions were stable for 1 month at 4 C.

Samples Blank samples of prawns and fish were obtained from Brazilian Federal Inspection Services (SIF), food national inspection service managed by Brazilian Ministry of Agriculture, collected in several fisheries farms. Fish samples were composed by Sarotherodon niloticus muscle tissue. Honey blank samples were obtained from several beekeepers, which are under SIF inspection system. Honey samples from distinct geographical origin and floral sources were used in validation procedures, to evaluate ruggedness.

Instrumentation and conditions Liquid chromatography Chromatography was performed on an Agilent 1100 Series LC system (Agilent Technologies, Santa Clara, California, USA) with a vacuum degasser and autosampler. The separation was achieved using two distinct columns. In the first, a Luna 5 mm C18 column, 150 4.6 mm (Phenomenex, Torrance, California, USA) was used. Alternatively, the method was also validated in a second scheme of HPLC separation, using a column XTerra end-capped 3.5 mm column, 100 2.1 mm (Waters, Milford, Massachusetts, USA). Injection volume was 20 mL and the analysis was carried out with gradient elution using (A) water and (B) acetonitrile as the mobile phase at a flow rate of 0.80 mL min 1 (Luna column) or 0.30 mL min 1 (XTerra column). Gradient was applied with an initial step composed by 90% of solvent A and 10% of solvent B holding for 3 min; in a second step, the mixture was composed by 50% of solvent A and 50% of solvent B, holding for 2 min. Then, the composition was set to initial conditions and holding for more 5 min, to a total time of 10 min for each run. Equilibrium time was 4 min in the same composition of the initial step. Gradient composition and run time were the same for both LC columns.

Standard solutions Stock solutions at a concentration of 1 mg mL 1 were prepared by dissolving CAP and TAP in methanol. Working standard solutions at concentrations of 10 mg mL 1 and 10 ng mL 1 for CAP were prepared by diluting the stock solutions with methanol. A d5-CAP internal standard solution of 6 ng mL 1 was prepared by dissolving the ampoule content (100 mg mL 1) using methanol until the desired concentration. TAP, which was tested as an alternative internal standard, was also diluted to obtain a 6 ng mL 1 solution. Stock solutions were stable for

Mass spectrometry LC–MS/MS system used was an API 5000 triple– quadrupole mass spectrometer (Applied Biosystems, Foster City, California, USA) that was connected to LC system via an electrospray ionisation (ESI) interface. Analysis of CAP was performed in negative ionisation mode. For quantitative purposes, samples were analysed by multiple reaction monitoring (MRM) mode. MRM parameters for the optimal yield of product ions were defined in individual time windows for analyte and internal standard as they eluted from

552

F. Barreto et al. Table 1. LC–MS/MS parameters for CAP analysis in ESI mode.

tR (min)

Parent ion (m/z)

Daughter ions (m/z)

Declustering potential (V)

Collision energy (eV)

CAP

5.44

321

d5-CAP

5.43

326

152a 257 121 157a 262

24 16 18 24 16

Analyte

Notes: LC, liquid chromatography; MS, mass spectrometry; CAP, chloramphenicol; ESI, electrospray ionisation. a Quantification ion.

the LC column (Table 1). All data were acquired using Analyst (version 1.4.2) software (Applied Biosystems).

(water–acetonitrile, 90:10) and transferred to an LC–MS/MS autosampler vial.

Sample preparation

Calibration Calibration curves were constructed using analyte/ internal standard peak area ratio versus concentration of analyte. Matrix-matched calibration curves were used through all studies, prepared at six spiking levels over ranges of 0.1–1.0 mg kg 1 for CAP. Internal standard was added at 0.3 mg kg 1. Spiked samples were pre-treated in three triplicates at each spiking level as described above and analysed by LC–MS/MS as described.

Fish and prawns Homogenised tissue (1.0 g) was weighed into a 50-mL polypropylene centrifuge tube and spiked with 50 mL of d5-CAP internal standard solution (6 ng mL 1) to obtain a concentration of 0.3 ng g 1. Sample was vortexed for 30 s and allowed to stand for 20 min. After addition of 5 mL of acetonitrile, the mixture was vortexed for approximately 15 s and shaken in a mechanical orbital shaker for 20 min (at 180 rpm, approximately). Then, samples were centrifuged for 5 min at 4000 rpm. Supernatant was transferred to another tube and 5 mL of chloroform was added and mixture was vigorously vortexed for 15–20 s. Agitation and centrifugation were performed again, also described above. After centrifugation for 5 min at 2000 rpm, the chloroform layer was discarded. Acetonitrile phase was evaporated to dryness under nitrogen stream in a water bath at 40–45 C. Residue was re-constituted with 1 mL of mobile phase (water– acetonitrile, 90:10) and transferred to an LC–MS/MS autosampler vial.

Honey Homogenised honey (1.0 g) was weighed into a 50-mL polypropylene centrifuge tube and spiked with 50 mL of d5-CAP internal standard solution (6 ng mL 1) to obtain a concentration of 0.3 ng g 1. Sample was dissolved with 1 mL of hot water (approximately 40 C) and vortexed for 30 s and allowed to stand for 20 min. After addition of 5 mL of ethyl acetate, the mixture was vortexed for 1 min and centrifuged for 5 min at 2000 rpm. An aliquot of 500 mL was transferred to a 15-mL centrifuge tube and evaporated to dryness under nitrogen stream at 40–45 C. Residue was reconstituted by 1 mL of mobile phase

Decision limit (CC ) and detection capability (CCb) Decision limit and detection capability – respectively CC and CC – were parameters proposed by the 2002/657/EC Decision for veterinary drugs residues methods. According to this document, more than one method can be used for determination of these parameters. In the present work, CC and CC were determined by matrix calibration curve procedure using the method proposed by Nicolich et al. (2006), which was recently also applied for analysis of CAP residues by other researchers (Zhang, Liu, et al. 2008). For this approach, samples spiked at levels of 0.30, 0.45 and 0.60 mg kg 1 were prepared in six replicates on three different days.

Results and discussion Optimisation of LC–MS/MS analysis Optimisation of LC separation for CAP and d5-CAP was performed testing different mobile-phase compositions and gradient elution conditions. Optimal conditions were described in the Instrumentation and Conditions section. Two distinct arrays for LC separation were tested and both showed

Food Additives and Contaminants satisfactory performance. In the first tests, we used a Luna C18 column 5 mm, 150 4.6 mm (Phenomenex), with a relatively high mobile-phase flow (800 mL min 1). In the second array, using a lower flow (300 mL min 1) and maintaining mobile phase composition and gradient, a XTerra column end-capped 3.5 mm, 100 2.1 mm (Waters) showed very similar results in terms of peak shape, but with distinct retention time. Considering that CAP and d5-CAP were halogencontaining molecules, use of negative mode for ESI was a logical choice. Deprotonated molecular ions [M H] were selected as precursor ions for CAP and d5-CAP in negative mode. For CAP, three different mass transitions were monitored using the conditions given in Table 1. The most abundant product ion (m/z 321 4 152) was used for quantification and the other two transitions were used as qualifiers, in agreement of identification points criteria proposed in the 2002/657/EC Decision. Determination of the internal standard d5-CAP was based on two transitions. Optimisation of sample preparation Because of the complexity of honey matrix, which contained large amounts of sugars, enzymes and proteins, CAP extraction using organic solvent was used in order to reduce the co-extractives from samples. Ethyl acetate gives efficient extraction as well as high recoveries for CAP and d5-CAP for honey samples (Martins Junior et al. 2006). Evaporation of only an aliquot of the organic extract showed a decrease of matrix interfering effects in comparison with evaporation of whole extract. Previous dissolution of samples with hot water produced a complete contact between matrix and extraction solvent, increasing effectiveness of the procedure. For prawn and fish samples, acetonitrile produced cleaner extracts, with a very satisfactory protein precipitation, but it was still necessary to use a de-fatting step. For fat removal, chloroform was tested and cleaner extracts were obtained with this solvent. No significant advantages were obtained by repeating the de-fatting step; therefore, liquidâ&#x20AC;&#x201C;liquid extraction with chloroform was performed just once. MRM chromatograms for a spiked sample of prawn are shown in Figure 1. Total ion chromatogram (TIC) of a honey blank sample is shown in Figure 2. Method validation Identification and confirmation According to 2002/657/EC Decision, confirmation of banned substances of Group A, from the Annex I of the Directive 96/23/EC such as CAP requires a minimum of four identification points. The four identification points were obtained using LCâ&#x20AC;&#x201C;MS/MS

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with one precursor and two product ions. Thus, performance criteria for confirmation were fulfilled. Table 2 shows ion ratios of two transition reactions of analyte in standard solutions and spiked samples together with maximum permitted tolerances given in the 2002/657/EC Decision. Ion ratios of each analyte in spiked samples fell within the maximum permitted tolerances for positive identification. Signal-to-noise ratios for each diagnostic ion was 43:1. Relative retention time of each analyte in spiked samples corresponded to retention time of CAP in standard solution within a tolerance of 2.5% according to 2002/657/EC Decision. Selectivity and sensitivity Method selectivity was checked by preparation and analysis of 20 blank samples and spiked samples (0.3 mg kg 1). No interference was observed at analyte and internal standard retention time in prawn samples. In honey, whatever of the origin and herbal profile of honey samples, several peaks eluted early before the CAP and d5-CAP peaks, but the retention time of analyte and internal standard showed a very low relative deviation standard (0.1% for CAP and 0.08% for d5-CAP, assuming the worst cases for both matrices); therefore, these interfering peaks can be discarded in the integration process on the basis of absence of confirmatory fragments and their retention time. For both matrices, CAP and d5-CAP show retention times of 5.44 and 5.43 min, respectively, for LC separation array 1. In LC separation array 2, using an XTerra column, a retention time of 6.7 min was obtained for CAP. Possible interference from other veterinary drugs was also evaluated. Four antibiotics and one parasiticide agent were separately studied (Table 3). Representative TIC chromatogram of spiked sample with CAP and interferences is shown in Figure 3. The sensitivity of the method was assessed by limit of detection (LOD) and limit of quantification (LOQ), obtained by signal-to-noise ratio analysis in 20 blank samples. LOD, defined as the signal equivalent to a signal-to-noise ratio of 3:1, was 0.02 mg kg 1. Similarly, LOQ corresponds to a signal-to-noise ratio of 10:1, and was 0.06 mg kg 1. LOD and LOQ were determined for all matrices, but values obtained for honey were extrapolated for the full method, since this matrix shows the worst case of noise. Linearity Response linearity was evaluated by matrix-matched calibration curves. Three calibration curves were made on 3 validation days. No significant differences were observed for slope and intercept among three calibration curves prepared on 3 validation days (p 5 0.05). Good linearity was obtained throughout

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Figure 1. Multiple reaction monitoring chromatograms for prawn sample spiked with 0.3 mg kg 1 of chloramphenicol (CAP): (a) CAP m/z 321 4 152, (b) CAP m/z 321 4 257, (c) CAP m/z 321 4 121, (d) CAP-d5 m/z 326 4 157 and (e) CAP-d5 m/z 326 4 262.

all tested concentrations for each analyte with corresponding correlation coefficients (r2) higher than 0.97. Our internal criteria, described in a specific SOP (Standard Operational Procedure) for matrix-matched calibration curves in concentrations lower than 10 mg kg 1 is r2 0.95. Matrix effects Statistical comparison of the three calibrations curves was performed. Curves were made in three different

ways: (I) curve prepared in pure solvent (mobile-phase initial composition); (II) matrix-matched curve, with all points spiked before extraction procedures and (III) tissue standard curve, with standard solution added after extraction procedures and applied to a previous blank sample extract. No significant differences were observed for slope and intercept among the curve types II and III; significant differences were observed among curve types I and II and among curve types I and III (p 5 0.05). These results showed that response was variable among processed and non-processed analytes;

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Figure 2. Total ion chromatogram of honey blank sample.

Table 2. Ion ratio parameters. MRM transitions ratio Average ion ratioa,b SD RSD Criteria Identification points

Stability 152/257

152/121

39 1.3 3.4 25 5.5

21 1.0 5.0 25 5.5

Notes: MRM, multiple reaction monitoring; RSD, relative standard deviation. a Ion ratio was obtained by R ¼ (area of less intense ion/area of more intense ion) 100%. b Average ion ratio of 30 spiked samples and standard solutions (n ¼ 15 injections for each type).

Stability of analyte in samples and in extracts was evaluated. For CAP stability in incurred samples, a prawn sample containing an assigned value of 0.81 mg kg 1 was analysed periodically over 10 months. Throughout the overall study period, no decrease in CAP signal was observed. Extracts of one validation batch (fish) were stocked in a freezer ( 20 C) and re-injected after 2, 7 and 14 days. No significant difference was observed in the signals. The stability study should be periodically evaluated and more studies must be performed for covering all matrices before the assessment of a definitive expiry period.

Table 3. Veterinary drugs used for interference study.

Compound Ciprofloxacin Norfloxacin Tetracycline Thiabendazole Florfenicol

Class

Concentrations added (ng mL 1)

Fluoroquinolones Fluoroquinolones Tetracyclines Benzimidazoles Amphenicols

50–100–150 50–100–150 50–100–150 50–100–150 50–100–150

thus, matrix-matched calibration curves were adopted for this study. Matrices produce ion suppression for both CAP and CAP-d5. Comparison between curves I (solvent) and III (tissue standard) showed a difference around 50% of peak areas, which result in a mean recovery of 56% for CAP and 48% for CAP-d5, when considering curve I as 100%. For this reason, CAP recoveries for extraction procedures in both matrices were calculated using only curve type III as parameter for whole concentration. As discussed elsewhere, honey is a more laborious matrix, with interference peaks that show distinct profiles from one sample to another. Plots of these data are shown in Figure 4.

Accuracy and precision Accuracy and precision were evaluated by determining recoveries of CAP in spiked samples using six or seven replicates on 3 validation days. According to the 2002/657/EC Decision, for CAP, spiking levels were 1 MRPL, 1.5 MRPL and 2 MRPL, which means, respectively, 0.3, 0.45 and 0.6 mg kg 1. Summarised data for all matrices are shown in Table 4.

CCa and CCb For CAP, CC and CC were calculated with calibration curves which give more weight to the lower spiking levels and thus allows a more representative estimation of standard deviation of the intercept (Sintercept) associated with uncertainly at lower levels. CC was calculated as concentration corresponding to intercept value þ 2.33 Sintercept, whereas CC was calculated as concentration corresponding to intercept value þ3.97 Sintercept. CC and CC obtained from three calibration curves were presented in Table 5. The mean of these values was considered as CC and CC

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Figure 3. Total ion chromatogram of blank sample of prawn spiked with 0.3 mg kg 1 of CAP and 100 mg kg 1 of florfenicol.

Ruggedness

Figure 4. Matrix effect evaluation. Plot of curve types I (solvent), II (matrix-matched) and III (tissue standard).

of the method: 0.04 and 0.06 mg kg 1, respectively, for prawns and fish and 0.05 and 0.09 mg kg 1 for honey.

Interlaboratory reproducibility During validation study, two proficiency tests provided by FAPAS were performed. Our laboratory received four honey samples and one prawn sample for CAP determination. Results based on z score showed complaints values for all samples. Comparisons between results are presented in Table 6.

The ruggedness of the method was evaluated for following parameters: analysts, LC columns, LC–MS/ MS systems, mobile-phase composition and gradient, internal standards. For analysts, three different analysts perform a full-batch analysis of the same fish sample. RSD was less than 10%. For LC columns, as described above, two arrays of LC separation were applicable for the CAP method, varying the mobile-phase flow. Another mobile phase using ammonium acetate 5 mM as aqueous phase was tested and showed similar results for water–acetonitrile mobile phase, and this last one was the choice. Considering that deuterated substances are relatively expensive and of variable availability, thiamphenicol (TAP) was evaluated as an alternative internal standard. TAP is another antibiotic of amphenicols class. TAP is a methyl-sulfonyl analogue of CAP that is not available as veterinary drug in Brazil. For this reason, TAP shows desired characteristics for an adequate internal standard for CAP determination. To evaluate TAP responses in comparison with CAP, blank samples of fish were spiked with both amphenicols in a range from 0.1 to 5.0 mg kg 1. This matrix-matched calibration curve shows a satisfactory correlation between CAP and TAP responses (r2 ¼ 0.9941). However, ionisation efficiency of CAP and TAP are different and the level of 0.3 mg kg–1, as used for d5-CAP, produce small peaks of TAP, in comparison

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Table 4. Accuracy, inter-day and intra-day precision parameters. Fortification levels (n ﾂｼ 6 for each batch; 3 batches for each level) 0.3 mg kg 1

0.45 mg kg 1

0.6 mg kg 1

Matrix

Parameters

Prawns

Average value Accuracy SD Intra-day (RSD %) Inter-day (RSD %)

0.30 100 0.05 16.9 12.6

0.29 98 0.02 6.2

0.28 93 0.04 13.4

0.39 87 0.04 10.0 12.2

0.46 102 0.07 15.2

0.41 92 0.00 1.0

0.53 88 0.03 6.0 6.0

0.53 88 0.04 8.0

0.52 87 0.02 4.4

Fish

Average value Accuracy SD Intra-day (RSD %) Inter-day (RSD %)

0.29 97 0.02 6.9 7.4

0.29 96 0.02 8.0

0.28 93 0.02 8.2

0.41 94 0.01 2.3 4.8

0.47 96 0.03 6.5

0.41 93 0.02 4.9

0.55 92 0.03 4.6 5.5

0.55 92 0.03 5.3

0.54 89 0.04 6.6

Honey

Average value Accuracy SD Intra-day (RSD %) Inter-day (RSD %)

0.36 120 0.02 6.2 14.6

0.26 88 0.02 6.4

0.30 100 0.02 6.5

0.61 136 0.04 6.8 22.5

0.38 85 0.02 5.1

0.45 101 0.06 13.5

0.71 119 0.02 3.3 16.7

0.52 87 0.04 8.4

0.52 87 0.04 7.2

Note: RSD, relative standard deviation.

Table 5. CC and CC values. Matrix

Slope

y-intercept

Sintercept

CC (mg kg 1)

Honey Fish Prawns

0.9464 0.9561 0.7421

0.0419 0.0548 0.0602

0.0206 0.0147 0.0108

0.05 0.04 0.04

0.09 0.06 0.06

Note: CC , decision limit; CC , decision capability.

Table 6. Proficiency test results for CAP. Matrix

Result (mg kg 1)

Assigned value (mg kg 1)

z score

Total no. of results

Honey

0.926 0.918 0.847 0.907 0.75

0.91

0.1 0.0 0.3 0.0 0.26

Prawns

0.81

Note: CAP, chloramphenicol.

with CAP peaks at the same concentration. For this reason, a spike level of 5.0 mg kg 1 was adopted for TAP used. Following, another matrix-matched calibration curve was prepared in triplicate, ranging from 0.1 to 2.0 mg kg 1 of CAP and maintaining the same concentration of TAP in all samples (5.0 mg kg 1). A satisfactory correlation value was observed (r2 ﾂｼ 0.9976), with RSD less than 11% for all levels. Average accuracy obtained was 101%. Thus, although deuterated compounds are more eligible as internal standards in mass spectrometry, TAP was available as an alternative.

Application Twenty-six real samples (5 honey samples and 21 fisheries samples), obtained from Brazilian Federal Inspection Services, and collected from several areas of Brazil, were analysed using the validated method. Detectable amounts of CAP were not found in any of these samples. Uncertainty Using a top窶電own approach, the uncertainty of the method was estimated. Quality control (QC) samples

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were included in each batch of analysis (n ¼ 6, at MPRL level). Accuracy values of these samples were inserted in a control chart, which besides providing a process surveillance, also allows the use of this value to calculate a combined standard deviation of the accuracy of these QC samples, which was multiplied by a coverage factor (k ¼ 2) to produce an expanded uncertainty for the method. In this case, QC data, which are produced for every routine analysis batches, provide a dynamic evaluation of the intra-laboratory reproducibility. Through this method, uncertainty of measurement of CAP at the MRPL level was established as 16% (or 0.3 0.048 mg kg 1).

Conclusions In this study, an LC–MS/MS method for the determination and confirmation of CAP residues in prawns, fish and honey was developed. The analyte was extracted from both matrices by liquid–liquid extraction without using any other clean-up technique such as SPE prior to the LC–MS/MS analysis. The method fulfilled the requirements for confirmatory criteria according to European Commission Decision 2002/ 657/EC requiring four identification points obtained for CAP with high sensitivity and selectivity. At different spiking levels, good accuracy and precision were obtained, which indicated that the method is suitable for the routine analysis in the National Residues and Contaminants Control Plan.

References Ali I, Aboul-Enein HY, Gupta VK, Singh P, Negi U. 2009. Analyses of chloramphenicol in biological samples by HPLC. Anal Lett. 42:1368–1381. Aresta A, Bianchi D, Calvano CD, Zambonin CG. 2009. Solid phase microextraction-liquid chromatography (SPME-LC) determination of chloramphenicol in urine and environmental water samples. J Pharmaceut Biomed Anal. 53:440–444. Brasil. Ministe´rio da Agricultura e Abastecimento. Instruçaõ Normativa n 9, de 27de junho de 2003. Dia´rio Oficial da Uniaõ, Brası´ lia, DF, 30 jun. 03. Seçaõ 1, p. 1–2, 2003. European Commission. 2002. Commission Decision 2002/ 657/EC of 12 August 2002: implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off J Eur Comm. L221:8–36. European Commission. 2010. Commission Regulation 37/2010 of 22 December 2009: on pharmacologically active substances and their classification regarding maximum residue limits in foodstuffs of animal origin. Off J Eur Comm. L15:1–72. Guo L, Guan M, Zhao C, Zhang H. 2008. Molecularly imprinted matrix solid-phase dispersion for extraction of chloramphenicol in fish tissues coupled with high-

performance liquid chromatography determination. Analyt Bioanalyt Chem. 392:1431–1438. Huang ZY, Yan QP, Zhang Q, Peng AH. 2009. Sample digestion for determining chloramphenicol residues in carp serum and muscle. Aquaculture Int. 17:69–76. Marsh JCW, Ball SE, Cavenagh J, Darbyshire P, Dokal I, Gordon-Smith EC, Keidan J, Laurie A, Martin A, Mercieca J, et al. 2009. Guidelines for the diagnosis and management of aplastic anaemia. Br J Haematol. 147:43–70. Martins Junior HA, Bustillos OV, Faustino Piers MA. 2006. Determination of chloramphenicol residues in industrialized milk and honey samples using LC-MS/MS. Quimica Nova. 29:586–592. Mauricio AdQ, Lins ES, Alvarenga MB. 2009. A National Residue Control Plan from the analytical perspective—the Brazilian case. Analytica Chimica Acta. 637:333–336. Nicolich RS, Werneck-Barroso E, Marques MAS. 2006. Food safety evaluation: detection and confirmation of chloramphenicol in milk by high performance liquid chromatography-tandem mass spectrometry. Analytica Chimica Acta. 565:97–102. Phillips CI. 2008. Risk of systemic toxicity from topical ophthalmic chloramphenicol. Scott Med J. 53:54–55. Rejtharova M, Rejthar L. 2009. Determination of chloramphenicol in urine, feed water, milk and honey samples using molecular imprinted polymer clean-up. J Chromatogr A. 1216:8246–8253. Rodziewicz L, Zawadzka I. 2008. Rapid determination of chloramphenicol residues in milk powder by liquid chromatography-electrospray ionization tandem mass spectrometry. Talanta. 75:846–850. Ro¨nning HT, Einarsen K, Asp TN. 2006. Determination of chloramphenicol residues in meat, seafood, egg, honey, milk, plasma and urine with liquid chromatographytandem mass spectrometry, and the validation of the method based on 2002/657/EC. J Chromatogr A. 1118:226–233. Shen J, Xia X, Jiang H, Li C, Li J, Li X, Ding S. 2009. Determination of chloramphenicol, thiamphenicol, florfenicol, and florfenicol amine in poultry and porcine muscle and liver by gas chromatography-negative chemical ionization mass spectrometry. J Chromatogr B. 877: 1523–1529. Silva LT, Druzian JI, Da Silva JR. 2010. Optimization and intralaboratorial validation of method for analysis of chloramphenicol residues in goat milk by GC/ECD. Quimica Nova. 33:90–96. Wihlborg AK, Boyd B, Kronauer S, Widstrand C, Trinh A. 2008. The highly selective extraction of chloramphenicol from shrimp using molecularly imprinted polymer solidphase extraction. Am Lab. 40:6–7. Zhang S, Liu Z, Guo X, Cheng L, Wang Z, Shen J. 2008. Simultaneous determination and confirmation of chloramphenicol, thiamphenicol, florfenicol and florfenicol amine in chicken muscle by liquid chromatographytandem mass spectrometry. J Chromatogr B. 875:399–404. Zhang C, Wang S, Fang G, Zhang Y, Jiang L. 2008. Competitive immunoassay by capillary electrophoresis with laser-induced fluorescence for the trace detection of chloramphenicol in animal-derived foods. Electrophoresis. 29:3422–3428.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 559–570

Simultaneous determination of chloramphenicol and florfenicol in liquid milk, milk powder and bovine muscle by LC–MS/MS D.R. Rezende, N. Fleury Filho* and G.L. Rocha Laboratory of Residues and Contaminants–LANAGRO-GO, Ministry of Agriculture, Livestock and Food Supply, Goiaˆnia, Goia´s, Brazil CEP 74674-025 (Received 29 November 2010; final version received 8 November 2011) A validated method based on European and Brazilian legislation is reported. It is applicable to the simultaneous determination of chloramphenicol (CAP) and florfenicol (FF) by LC-MS/MS in liquid milk, milk powder and bovine muscle. The chromatographic analysis is completed in 6 min and the extraction procedure is very simple, involving only one step liquid-extraction with ethyl acetate. Where it proved necessary to include clean-up, an efficient and rapid step using C18-dispersive solid was added. Initially, a complete validation was performed with liquid milk matrix; later the scope was extended to the other matrices through extending the inter-day precision (within laboratory reproducibility) RSD values. An internal standard (d5-CAP) was employed for quantitative purposes. The method was shown to have good accuracy and precision for determining CAP residues at the level of 0.3–0.6 mg kg 1 and FF residues at the level of 5–15 mg kg 1. Keywords: LC-MS/MS; veterinary drugs; residues; validation; chloramphenicol; florfenicol; MSPD

Introduction Chloramphenicol (CAP) and florfenicol (FF) are broad-spectrum antibiotics that are suitable for treatment of a variety of infectious organisms, both are phenicol antibiotics and comprise the phenicol group. They exert their action through protein inhibition and are effective in the treatment of several infectious diseases. This, together with their low cost and ready availability, has led to their extensive use since the 1950s in the treatment of animals all over the world, including food-producing animals (Ronning et al. 2006). In veterinary medicine, CAP has been shown to be a highly effective, well-tolerated antibiotic. However, it is not possible to establish a safe intake level for its residues or its metabolites in food. The possibility of disseminating resistant bacteria is also very important when discussing the impact of antibiotic residues in food (Nicolich et al. 2006). Moreover, CAP has displayed significant toxicological effects; it was found to produce blood dyscrasias in humans. In fact, it produces two distinct types of myelotoxicity. The less serious of these is a reversible bone marrow suppression because of mitochondrial damage that produces a mild anaemia. The more serious effect is bone marrow aplasia or aplastic anaemia with pancytopenia and cellular bone marrow (Woodward 2004). In 1987, the Joint FAO/WHO Expert Committee on Food Additives (JECFA) considered CAP as a drug *Corresponding author. Email: nelio.fleury@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.641161 http://www.tandfonline.com

with no observable effect level for aplastic anaemia, but it could not calculate an Acceptable Daily Intake (ADI) level. Almost the same conclusion was found by the Committee for Veterinary Medicinal Products and in consequence recommended its inclusion into Annex IV of Regulation No. 2377/90, thereby prohibiting its use in food animals in the EU (European Commission 1990). A zero tolerance level for this compound in edible tissues was established to protect the consumer (Ramos et al. 2003). As no maximum residue limit (MRL) could be established for CAP in animal-derived foods, it becomes necessary to provide harmonised levels for the control of these substances to ensure the same level of consumer protection throughout the EU. The European Commission defined a minimum required performance limit (MRPL) for CAP in food of animal origin at a level of 0.3 mg kg 1, MRPL means ‘‘minimum content of an analyte in a sample which at least has to be detected and confirmed’’ (European Commission 2003). In the same way, the United States established a zero tolerance for the presence of CAP in food products (FDA 1988). Because of the banning of CAP in food producing animals, florfenicol (FF), a fluorinated analogue of CAP, is used increasingly in aquaculture, livestock and poultry to treat diseases. It is a synthetically produced broad-spectrum antibacterial agent specifically developed for veterinary use. FF was demonstrated to be

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less toxic than CAP, the main reason for which is the absence of nitro group. However its side effects cannot be excluded and residues of FF drugs may therefore pose a health risk to consumers (Shen et al. 2009). Therefore, the use of FF is permitted, but controlled. To ensure the existence of FF antibiotics, the MRLs in various tissues have been defined by many countries (Wu et al. 2008). The EU established an MRL for FF at 200 mg kg 1 for bovine muscle, considering the marker residue as the sum of FF and its metabolites measured as florfenicol amine (FFA); in addition, the EU requires that the use of FF is not permitted for animals from which milk is produced for human consumption (European Commission 2010). Although an FF depletion study has demonstrated that many metabolites such as florfenicol amine, florfenicol oxamic acid and florfenicol alcohol are present, the same recent publication about FF depletion demonstrated that the most abundant residue present in fish, swine and poultry muscle is still parent FF (Li et al. 2006; Anado´n et al. 2008; Lim et al. 2010). Nevertheless, there are almost no publications concerning FF depletion and the presence of FF metabolites in milk. FF has been approved for the treatment of bovine respiratory disease in the United States (Schencka and Calleryb 1998). The FDA established a level of tolerance at 300 mg kg 1 for the marker residue FFA in cattle muscle (FDA 2011), and the National Drug Residue Milk Monitoring Program has set a level of concern of 10 mg kg 1 for parent FF in milk (FDA 1997). The most recent Brazilian Legislation (MAPA 2010) set the MRPL for CAP at 0.3 mg kg 1 for milk and bovine muscle; however, there are no MRLs established for FF. According to the Brazilian Veterinary Products Compendium (SINDAN), there are six formulated products that contain FF to treat bovine infections. The withdrawal periods before human consumption are 28 days for meat and 5 days for milk (SINDAN 2011). For these reasons and considering the low commercial viability of FFA in Brazil, we decided to work only with parent FF and adopt the low value of 10 mg kg 1 as a reference value for validation purposes for both matrices—milk and bovine muscle. During the past few years, several methods were proposed for the determination of CAP residue in various matrices, using different types of instruments (LC-MS/MS, GC-MS, ELISA, HPLC-UV, biosensor), most of them using liquid extraction followed by clean-up by solid-phase extraction (SPE) (Gantverg et al. 2003; Mottier et al. 2003; Ramos et al. 2003; Bogusz et al. 2004; Guy et al. 2004; Ashwin et al. 2005; Ferguson et al. 2005; Scortichini et al. 2005; Shen et al. 2005; Martins Ju´nior et al. 2006; Nicolich

et al. 2006; Ronning et al. 2006; Shi et al. 2007; Rodziewicz and Zawadzka 2008). On the other hand, few papers have been published describing method development and validation for simultaneous determination of CAP and FF in edible tissue such as poultry, fish, shrimp and bovine and porcine muscle (Pfenning et al. 2000; Van de Riet et al. 2003; Peng et al. 2006; Zhang et al. 2008; Chou et al. 2009; Shen et al. 2009; Luo et al. 2010). Especially regarding the analysis in milk, the number of articles are much more restrict. Pfenning et al. (1998) describe a method for simultaneous determination of CAP, FF and thiamphenicol (TAP) in milk, the extraction procedure was carried out by liquid extraction with acetonitrile purified by C18 SPE column, the extract was derivatised prior to analysis by GC with electron capture detection. More recently, Pezza et al. (2006) published a method for the determination of CAP, FF and TAP in milk by micellar electrokinetic chromatography. Both methods reported good values of recovery and precision; however, both the detection techniques employed did not meet all identification points request for confirmatory methods in monitoring programmes. This small number of papers reflect the need to develop new methods with high selectivity detectors as LC-MS-MS to unequivocally confirm simultaneously the presence of CAP and FF in milk. The isolation of compounds of interest from environmental, food or biological matrices is always a key step in the development of an analytical method. SPE is routinely used for clean-up and pre-concentration in the analysis of this kind of samples. Compared with liquid–liquid extraction, SPE has the advantages of simplicity, speed and less consumption of organic solvents. However, generic sorbents usually lack selectivity and are easily subjected to interference by non-target substances with similar characteristics (Shi et al. 2007). Matrix solid-phase dispersion (MSPD) extraction is a patented process first introduced in 1989 by Barker et al. for disrupting and extracting solid samples (Fernandes and Soares 2007). The novelty of the technique consisted in obtaining isolation of target analytes by dispersing tissues onto a solid support. The great interest for MSPD is due to the several advantages it offers, and its simplicity and flexibility. In fact, differently from classical extraction methods that require often clean-up steps, large amounts of samples, sorbents and organic solvents and thus are expensive and time consuming, MSPD is rapid, less manual-intensive and more eco-compatible. Moreover, the versatility of MSPD allows the applications of the process to a wide variety of analyte classes, such as drugs, pesticides, polychlorinated biphenyls, antibiotics and antibacterials, surfactants and naturally

Food Additives and Contaminants occurring compounds, in different matrices (Capriotti et al. 2010). Milk has always been considered a difficult matrix to analyse residues because of the presence of high levels of interfering compounds such as lipids, proteins and fatty acids. For that reason, it is always necessary to employ a clean-up step during the extraction procedure. In this work, we choose to use a C18 MSPD extraction to efficiently remove any interfering compound that may be present in milk. The objective of this research was to develop an LC-MS-MS method for simultaneous quantitation and confirmation of CAP and FF residues with an effective and rapid extraction procedure, based on MSPD from matrices such as milk powder, liquid milk and bovine muscle. The method has been validated based on the European Union (European Commission 2002) and Brazilian legislations (MAPA 2009) pertaining to the performance of analytical methods and the interpretation of results.

Materials and methods Chemicals and standards Analytically pure reagents and HPLC-grade solvents were used. Formic acid was obtained from J.T. Baker (Phillipsburg, NJ, USA). Methanol and ethyl acetate was obtained from Merck (Darmstadt, Germany). Acetonitrile was purchased from Carlo Erba (Italy). Ammonium acetate was obtained from DinaË&#x2020;mica (Diadema, SP, Brazil). Sodium sulfate anhydrous was acquired from Merck. Bounded-silica Bondesil C-18 was obtained from Varian (Santa Clara, CA, USA). Deionised water was generated by a water purification system (Simplicity UV, Millipore, Bedford, MA, USA). Standards were purchased from the following suppliers: chloramphenicol (CAP) from Riedel-de HaeÂ¨n (Seelze, Germany), florfenicol (FF) from Sigma-Aldrich (St. Louis, MO, USA) and chloramphenicol-d5 (CAP-d5) from Dr. Ehrenstorfer (Augsburg, Germany). CAP-d5 was used as internal standard (IS).

Solutions Stock standard solutions of CAP and FF were prepared in acetonitrile at a concentration of 100 mg mL 1. The CAP stock solution was further diluted in methanol/water (50:50 v/v) to yield appropriate intermediate solution. This intermediate solution together with the FF stock solution was used for preparing the spiking standard solutions. Intermediate and working solutions of the IS were prepared in methanol/water (50:50 v/v) by diluting the stock solution acquired commercially.

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Samples For spiking studies, blank matrix was previously shown to be free of any detectable amounts of the studied antibiotics. Bovine muscle samples were supplied by the local slaughterhouse. Liquid milk and milk powder samples were supplied by supermarket. The tissues were minced and stored at 18 C. The milk powder were stored in the refrigerator ( 4 C) and dissolved in water (13 g/100 mL) before analysis. The liquid milk was stored at 18 C. All the samples were kept at room temperature and homogenised before analysis.

Extraction and MSPD clean-up The extraction procedure was adapted from Nicolich et al. (2006) in which an MSPD clean-up step was necessarily incorporated to optimise the extraction efficiency. Minced bovine muscle (2 g) or 2 mL of milk (liquid and powder) were placed into a 15-mL polypropylene centrifuge tube. After that, 100 mL of internal standard working solution was added and the samples vortexed for 1 min. Selected blank samples were spiked with known quantities of CAP and FF to work as quality control samples and to evaluate the method recovery and precision during the validation procedure. After 10 min of equilibration, 0.8 mL of formic acid solution 10 mmol L 1 (in water) was added to milk samples and 2 mL to muscle samples, and they were homogenised for 15 sec. Sequentially, 0.8 g of sodium sulfate was introduced to the tube. The samples were shaken vigorously for 1 min. Consecutively, 4 mL of ethyl acetate was added and the samples were shaken vigorously again for another 1 min. The mixture was then centrifuged for 5 min at 4000 rpm. The supernatant was transferred to another tube containing 0.25 g of C-18 dispersive solid for extract clean-up. After vortex and centrifugation, the supernatant was evaporated to dryness at 40 C, under nitrogen flow. The residues were dissolved with water/ methanol (50:50, v/v) solution to a final volume of 400 mL. The tubes were vortexed and introduced in an ultrasonic bath for 5 min. Finally, the extracts obtained were filtered through a 0.20-mm PTFE filter before injecting it into the LC-MS/MS system.

Instrumentation The chromatographic analyses were carried out using a Waters Alliance (Waters, Milford, MA, USA) HPLC system equipped with vacuum degasser and autosampler. The chromatographic separation was carried out on a Phenomenex (Torrance, CA, USA) Luna C18 column (50 2.0 mm, particle size 5 mm). The mobile phases were 5 mM ammonium acetate in water/

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methanol solution (95:5, v/v) (mobile phase A) and 5 mM ammonium acetate in methanol/water solution (95:5, v/v) (mobile phase B). The gradient elution program started with 70% of A kept during 0.2 min with linear decrease to 10% of A in 1 min and kept at 10% of A for 5 min. The column was held in the initial mobile phase for 8 min to equilibrate before every chromatographic analysis. Mobile-phase flow rate was set to 0.4 mL min 1, and the injection volume was 30 mL. The HPLC system was connected to a triplequadrupole tandem mass spectrometer Quattro Premier XE, Waters with ESI source. The following instrument conditions were used: source temperature, 120 C; capillary voltage, 2.5 kV; desolvation temperature, 300 C; cone gas flow, 50 L hr 1; desolvation gas flow, 700 L hr 1. Nitrogen gas was used as cone and desolvation gas and argon gas was used as collision gas. The detection was performed in the selective reaction monitoring (SRM) mode with ESI source in the negative ionisation mode. MS/MS parameters were optimised by infusing each compound separately at a flow rate of 10 mL min 1. After determining the best conditions for isolating the precursor ion, two most intense product ions were selected per analyte and subsequently also optimised. The most intense product ion was selected as quantitative ion and another ion was selected as confirmatory ion. It is sufficient to obtain at least four identification points as required by the 2002/657/EC decision (European Commission 2002). The transitions of each compound and its CONE voltage and collision energy (CE) are summarised in Table 1. As can be seen, CAP-d5 was monitored by two transitions, but only the most intense one was used as an internal standard for quantification propose. Validation protocol The complete validation according to the 2002/657/EC decision was performed in liquid milk matrix.

The other matrices (milk powder and bovine muscle) were submitted to comparative validation according to the laboratory’s internal guideline. A series of parameters were determined: linearity, matrix effect, ion ratio precision, selectivity, stability, limit of detection (LOD) and quantification (LOQ), precision, accuracy, decision limit (CC ) and detection capability (CC ). Linearity and matrix effect The study of calibration curve linearity was conducted according to Souza and Junqueira (2005), in which the ordinary least squares method (OLSM) is the preferential regression model as it is a well-known and very understandable regression model. A consequence of the indiscriminate use of the OLSM is the omission of the assumption tests and frequently this is an important source of errors in analytical chemistry. Because of this, fitting a calibration function by OLSM requires several issues related to the residuals (normality, homoscedasticity, independency) and to the model (regression significance, linearity deviation). For this, calibration curves with five concentration levels in triplicate (0.5, 1.0, 1.5, 2.0 and 2.5 times the MRL/MRPL) were prepared. The linearity assessment was tested both in solvent and in matrix-matched calibration curves (MMCCs) for liquid milk, milk powder and bovine muscle. Any matrix effect was estimated by comparing calibration curves. The values of intercept (a) and slope (b) of calibration curves were compared to estimate the influence of the matrix components in the analytes response (Souza et al. 2007). Four calibration curves were prepared: in solvent, in liquid milk extract, in milk powder extract and in bovine muscle extract; for the last three, the MMCC matrix extract was fortified after the extraction and clean-up steps. All four calibration curves were compared to each other to determine which calibration curves are statistically similar. Stability

Table 1. LC/ESI-MS/MS parameters. Compound Precursor ions Product ions CONE (V) CE CAP

321.3

356.2

CAP-d5

326.3

152.2a 257.2 336.2a 219.2 157.2b 262.3

30 20 30

20 10 10 10 20 10

Notes: LC, liquid chromatography; ESI, electrospray ionisation; MS, mass spectrometry; CAP, chloramphenicol; FF, florfenicol; CAP-d5, deuterated chloramphenicol-d5; CE, collision energy. a Quantitative ion. b Only this ion was used as an internal standard’s daughter selective reaction monitoring.

The stability of the analytes was determined by chromatographic signal comparison between stored solutions and freshly prepared solutions. This procedure was done for stock, spiking and working solutions. To increase the test reliability, many replicates of each solution were prepared. Every replicate was injected two times, totaling more than six measured values of each solution. Finally, the obtained data were compared by significance tests as analyses of variance (ANOVAs) and Student’s t test. Ion ratio precision and selectivity The ion ratio between quantitative and confirmatory transitions was collected for more than 100 analyses

Food Additives and Contaminants to determine its precision as relative standard deviation (RSD) for each analyte. The assessments of selectivity were based on the analysis of blank matrix samples. Considering the high selectivity of an MS/MS analysis in the SRM mode, the laboratory adopted a practical approach to reduce the number of validation experiments and still demonstrate the method’s fitness for purpose. So, during the validation procedure, 12 blank liquid milk, 6 blank milk powder and 6 blank bovine muscles (totaling 24 blank samples) were analysed to prove that there were no interference peaks in the analytes’ retention time. Precision and accuracy Precision and accuracy were evaluated by analysing spiked blank matrix. The samples were spiked at 0.5, 1.0, 1.5 and 2.0 times the MRL/MRPL analysing six replicates each, in three different days by two different analysts. Based on 2002/657/EC decision, the first three levels were used for FF (permitted drug) and the last three levels were used for CAP (banned drug) validation. At the end of the validation experiments, it was possible to evaluate the accuracy as recovery values (n ¼ 18), and the precision as intra-day precision (repeatability) and within-laboratory precision (reproducibility). The spiked samples were quantified using MMCC daily prepared in six concentration levels (0.0, 0.5, 1.0, 1.5, 2.0 and 2.5 times MRL/MRPL). The analytical range was 0–0.75 mg kg 1 for CAP and 0–25 mg kg 1 for FF. For milk powder and bovine muscle, just one day (intra-day precision) was carried out, following the same procedure described previously. The procedure adopted to validate the method for these matrices was an in-house practical approach, used to demonstrate the applicability of the method to these other matrices. For this, a statistical test (ANOVA) was applied comparing the values obtained on liquid milk reproducibility against the values of powder milk and bovine muscle repeatability. This comparison was evaluated in order to statistically prove the method equivalence for both matrices. CC , CC , LOD and LOQ According to 2002/657/EC European decision, the decision limit (CC ) is defined as ‘‘the limit at and above which it can be concluded with an error probability of that a sample is non-compliant’’ and the detection capability (CC ) as ‘‘the smallest content of the substance that may be detected, identified and/ or quantified in a sample with an error probability of ’’. In fact, this concept had already been introduced in the ISO/11843-1 normative document (ISO/11843-1 1998) to propose a method fixing a limit from which

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a system can be declared different from its basic state (Antignac et al. 2003). For CAP, as a banned drug, the value of error is set at 1% while for FF the error is set at 5%; the

error is 5% for both analytes. To estimate the values of CC and CC for CAP and FF in liquid milk, the laboratory used the data obtained in the reproducibility assays. With those data, a calibration graph was constructed plotting the expected values (1.0, 1.5 and 2.0 MRPL for CAP and 0.5, 1.0 and 1.5 MRL for FF) versus the obtained value (from the reproducibility study), so the calibration graph presented 18 points per level totaling 54 points. From that graph, the values of CC and CC were calculated according to ISO/11843-1. For CAP, the values of CC were estimated as the corresponding concentration at the y-intercept plus 2.33 times the standard deviation of the withinlaboratory reproducibility of the intercept, and the values of CC were estimated as the corresponding concentration at the decision limit (CC ) plus 1.64 times the standard deviation of the within-laboratory reproducibility of the mean measured content at the decision limit. For FF, the value of CC were estimated as the corresponding concentration at the permitted limit plus 1.64 times the standard deviation of the withinlaboratory reproducibility, and the value of CC

were estimated as the corresponding concentration at the value of the decision limit (CC ) plus 1.64 times the standard deviation of the within-laboratory reproducibility. Because only a repeatability procedure was carried out for milk powder and bovine muscle, an alternative approach was taken to establish the values of CC and CC for these two matrices. The procedure adopted in these cases was mathematically similar to those explained above; the difference was that the standard deviation of the within-laboratory reproducibility obtained during liquid milk validation was combined with milk powder and bovine muscle repeatability data since both matrixes showed similar precision values. The limits of detection (LOD) and quantification (LOQ) were obtained using samples with low concentrations of analytes (blank samples fortified at the lowest level), the LOD was estimated at 3 times the lowest-level (0.5 MRL for FF and 1.0 MRPL for CAP) standard deviation of the within-laboratory reproducibility and the LOQ as 10 times the lowestlevel standard deviation of the within-laboratory reproducibility. Method uncertainty The method uncertainty at the MRL/MRPL was evaluated considering two sources of uncertainty: the uncertainty associated with the calibration curve and

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uncertainty associated with the precision as those two sources shown to be the ones that most contributed to the total uncertainty of the method.

Results and discussion Extraction and clean-up optimisation Two methods found in the literature that offered fast and simple extraction and clean-up procedures were chosen for evaluation. The first method employs acetonitrile as extraction solvent followed by a cleanup step with chloroform (Martins JuÂ´nior et al. 2006). The second method involves two extraction steps with ethyl acetate and no clean-up step (Nicolich et al. 2006). The last method had better recovery and precision. Although this method was quick and easy, some disadvantages were observed. During the liquid extraction, an emulsion was formed between the organic and the aqueous phases which adversely affected the precision of the method since the organic layer volume varied from sample to sample. In addition, a chromatographic peak interference at CAP and CAP-d5 retention times was detected, which was observed in both SRM transitions, which interfered in the analyte confirmation. These factors affected the efficiency of the method, especially for CAP analysis. To deal with those problems and to obtain an efficient and quick extraction procedure, same modification using MSPD steps were tested. A previous step employing the addition of anhydrous sodium sulfate at the extraction stage was included to prevent any emulsion formation during

the liquid extraction and to improve the analyte partitioning to the organic phase. By using anhydrous sodium sulfate, it was possible to ensure recovery of analytes using only one liquid extraction step with 4 mL ethyl acetate, improving the speed of the method. The following dispersive solids were tested: C18 (bounded silica) and primary and secondary amine (PSA) in order to improve the extraction clean-up by eliminating any co-extraction interfering substances. The use of C18 dispersive solid combined with the addition of sodium sulfate showed the best results by eliminating any emulsion formation and removing the interfering substance at CAP and CAP-d5 retention time (Figure 1). The use of PSA dispersive solid did not eliminate the interfering substance. Observing the chromatograms on Figure 1(a) and (c), it can be concluded that a clean-up step was extremely necessary to eliminate that non-resolved, broad interfering peak. Besides, that interference can compromise the resultsâ&#x20AC;&#x2122; precision and accuracy for quantitative and confirmatory purposes. Analysing the chromatograms in Figure 1(b) and (d), it was confirmed that a simple and rapid MSDP clean-up step was sufficient to remove the interferer compound. By those results, it could be concluded that the interfering substance probably was a lipophilic compound that could be retained on a non-polar dispersive solid such as C18. On the other hand PSA, which has a basic character, interacts with polar and/or acid organic molecules, not affecting the interfering compound.

Figure 1. Multiple reaction monitoring (321.3 4 152.2) chromatograms of blank milk extract: no clean-up (a); after clean-up (b). Milk extracts fortified at 0.3 mg kg 1 of CAP: no clean-up (c); after clean-up (d).

Food Additives and Contaminants HPLC-MS/MS analysis CAP being a banned drug, the minimal concentration level to be detected and quantified is 0.3 mg kg 1. Therefore the intensity of the CAP chromatographic peak was quite small; anyway it presented a signalto-noise ratio higher than 10. The chromatographic analysis was completed in 6 min. CAP and CAP-d5 had a retention time of 2.2 min and FF had a retention time of 1.8 min. After the clean-up step, there was no more chromatographic interference.

Method validation Study of linearity and matrix effect The internal standardisation method was used in all calibration curves. The linearity in the concentration range studied (CAP: 0–0.75 mg kg 1; FF: 0–25 mg kg 1) was proven by employing statistical tests. The residuals distribution graph demonstrated that the data were homoscedastic; no trends were observed and no linearity deviation could be seen (Figure 2). After these, it was possible to confirm that all analyte calibration curves (in solvent and matrix matched) were approved in the linearity assessment statistical tests (tests are omitted in this paper), demonstrating that the OLSM is a good assumption to describe all curves. The correlation coefficients (rs) were higher than 0.99 (Table 2) and the regressions were significant at a confidence level of 95%. The linear regression results are presented in Table 2. To demonstrate the regression uncertainty at the MRPL/MRL, the regression standard deviation was calculated for each MMCC according to Miller and Miller (2005). The results were expressed as RSD. For quantification purposes, MMCC should be used, once it was proven that matrix effect was present when comparing the compound’s response in solvent and in matrix. On the other hand, there was no matrix effect between liquid milk and milk powder as can be seen in Figure 2. In consequence, liquid milk and milk powder samples can be quantified using calibration curves prepared in either of these matrices. Nevertheless, bovine muscle MMCC was too different from milk MMCC. Although for both MMCC an internal standard was employed, it was not enough to eliminate all interference that arises from matrix effect, especially for FF. Based on these results, this type of sample matrix must be analysed on its own MMCC. The solvent and matrix-matched curves obtained in matrix effect studies are demonstrated in Figure 2. Stability The results of t test applied to solutions showed that the stock and intermediate solutions are stable for 1 year. The spiking and working solutions were shown

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to be stable for 6 months. All solution stability was proven in low temperatures ( 18 C) and protected from light.

Selectivity and ion ratio precision The method selectivity was proven because no interfering peak was observed in the analytes’ retention times for liquid milk, milk powder and bovine muscle blank samples. Ion ratios between the quantifier ion peak area and the qualifier transition peak area for each analyte were automatically calculated by the HPLC-MS/MS software (Masslynxs 4.1). The RSD of the ion ratio is an indication of stability when comparing to the tolerance values. The procedure adopted demonstrates that even for different matrices, on different days and different analyte concentrations, the values of ion ratios were stable. As can be verified (Table 3), the ion ratios’ precision, estimated as RSD values, were below the tolerance established by 2002/657/EC, proving that the detector provided enough identification points to minimise false-positive results and that those criteria were stable during all validation and routine procedures.

Precision and accuracy The method showed accuracy values between 89% and 107% for all analytes in all spiked levels (Table 4) for both matrices. The good accuracy values were a consequence of the method’s internal standardisation, which corrects analyte losses during sample extraction and clean-up, thus improving method trueness. The method precision was estimated by two sources: the method repeatability showing the intra-day precision calculated as relative standard deviation for repeatability (RSDr) obtained on a same batch analysis and the method within-laboratory precision representing the method reproducibility, calculated as the relative standard deviation for reproducibility (RSDR) obtained on 3 different days by two different analysts. For bovine muscle and milk powder, only RSDr value was obtained in a 1-day repeatability assay. To verify the possibility of extending the RSDR obtained in the liquid milk matrix to the other matrices, an ANOVA test was applied to the data. The test comparing the variance intra-matrix and inter-matrix demonstrated that there were no statistical differences between them, and both matrices showed equivalent performances. So the RSDR of the liquid milk validation can be used for these other matrices. The precision values (Table 4) lying between 4% and 15%, below the maximum permitted value of 22%, demonstrate that the method is very precise even under different conditions and different matrices.

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Matrix–matched calibration curves

2.5 Milk powder y = 2.858x – 0.0023 R2 = 0.996 2 Liquid milk y = 2.727x + 0.0444 R2 = 0.990 1.5 Area ratio

Bovine Muscle y = 2.6005x + 0.0152 R2 = 0.9976 1 Solvent y = 2.4974x + 0.0126 2 R = 0.9918 0.5 Linear (Milk powder) Linear (Liquid milk ) Linear (Bovine muscle) Linear (Solvent )

0 0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

Concentration (µgkg–1) (b)

Solvent residual plot

(c)

Bovine muscle residual plot 0.08

0.15

0.06 0.04

0.05 0 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 –0.05

Residual (êi)

0.1

0.02 0 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 –0.02 –0.04 –0.06

–0.1

–0.08

–0 15

–0.1 CAP Concentration (µg kg–1)

(d)

Liquid milk residual plot

0.150

(e)

0.050 0.000 0.000 –0.050

Milk powder residual plot

0.100

0.200

0.300

0.400

0.500

0.600

0.700

–0.100

0.800

Residual (êi)

0.100

0.150

CAP Concentration (µgkg–1)

0.050 0.000 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 –0.050 –0.100 –0.150

–0.150 CAP Concentration (µg kg–1)

CAP Concentration (µg kg–1)

Figure 2. Linearity assessment and matrix effect of for solvent, liquid milk, milk powder and bovine muscle calibration curve. Comparison of the calibration curve for each type (a); residual plot for solvent calibration curve (b); bovine muscle matrixmatched calibration curve (MMCC) (c); liquid milk MMCC (d); and milk powder MMCC (e).

LOD, LOQ, decision limit (CC ) and detection capability (CC ) The values of LOD and LOQ were estimated as 3 and 10 times, respectively, the lowest validated level standard deviation obtained on the within-laboratory precision (Table 2). Liquid milk matrix showed LOQ above CAP MRPL (0.3 mg kg 1); nevertheless, in practice it was proven that CAP was quantified with enough accuracy and precision at that level.

The values of CC (decision limit) and CC

(detection capability) were calculated according to ISO 11843:1997, where a calibration curve is used by plotting the calculated concentration against the added concentration. All the data used were obtained from the precision experiments. Table 5 presents the data for all matrices. As CAP is a banned drug, CC and CC values must be below the MRPL value. As can be seen from Table 5, all values of CC and CC obtained were satisfactory.

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Food Additives and Contaminants Table 2. Calibration curve parameters for all matrices and theoretical values of LOD and LOQ. Linearity Matrix Liquid milk Powder milk Bovine muscle

Analyte

LOD (mg kg 1)

LOQ (mg kg 1)

Equation

RSDr (%)a

CAP FF CAP FF CAP FF

0.102 1.646 0.066 0.939 0.072 0.801

0.339 5.485 0.22 3.13 0.239 2.669

y ¼ 0.8869x þ 0.0353 y ¼ 34.658x 0.663 y ¼ 0.8182x þ 0.0444 y ¼ 34.418x 0.3387 y ¼ 0.7801x 0.0152 y ¼ 27.857x 0.0386

0.9973 0.9994 0.9949 0.9988 0.9988 0.9995

5.4 4.3 5.8 2.7 1.7 2.8

Notes: LOD, limit of detection; LOQ, limit of quantification; CAP, chloramphenicol; FF, florfenicol; MRL, maximum residue limit; MRPL, minimum required performance limit. a Regression relative standard deviation at the MRPL/MRL.

Table 3. Ion ratio precision. Ion ratio Compound

1st Transition

2nd Transition

Mean (n 4 100)

RSD (%)

Tolerance (%)a

CAP FF

321.3 4 152.2 356.2 4 219.2

321.3 4 152.2 356.2 4 336.2

0.82 0.089

0.116 0.015

14.089 17.159

20 50

RSDR (%)

Accuracy (%)

Notes: RSD, relative standard deviation; CAP, chloramphenicol; FF, florfenicol. 2002/657/EC.

Table 4. Precision and accuracy results (n 4 18).

Matrix

Accuracy (%)

RSDr (%)

RSDR (%)

Accuracy (%)

0.3 mg kg 1 CAP Liquid milk Milk powder Bovine muscle

89.27 93.06 102.58

12.36 7.89 7.75

0.45 mg kg 1

12.79

92.72 93.37 98.46

5.0 mg kg 1 FF Liquid milk Milk powder Bovine muscle

100.65 104.4 106.15

10.61 5.99 5.03

RSDr (%)

10.26 11.93 4.48

94.66 104.9 97.1

14.15 8.12 4.48

RSDR (%)

0.6 mg kg 1 98.61 97.42 103.56

10.95

10.0 mg kg 1

11.02

RSDr (%)

10.37 4.32 6.58

11.84

15.0 mg kg 1 98.41 98.01 91.00

14.15

10.12 8.72 4.15

16.60

Note: RSDr, relative standard deviation for repeatability; RSDR, relative standard deviation for reproducibility; CAP, chloramphenicol; FF, florfenicol.

Table 5. Results of the method validation for determining CAP and FF in different matrices. Matrix Liquid milk Milk powder Bovine muscle

Compound

MRL/MRPL (mg kg 1)

CC (mg kg 1)

Uncertainty (%)

CAP FF CAP FF CAP FF

0.3 10 0.3 10 0.3 10

0.13 12.65 0.14 12.59 0.15 12.77

0.21 15.34 0.24 17.66 0.26 18.27

29.6 20.08 29.93 30.99 28.43 30.78

Note: MRL, maximum residue limit; MRPL, minimum required performance limit; CC , decision limit; CC , detection capability.

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The approach adopted by the laboratory to estimate the values of CC and CC for powder milk and bovine muscle was a justified approximation that generates reasonable values with only a few experiments. Method uncertainty The global uncertainty was evaluated assuming that both contributions were independent of each other. The calibration curve RSD was used to estimate the uncertainty associated with the calibration curve (Table 2) and the within-laboratory precision (RSDR) was used to estimate the uncertainty associated with the method precision; both values were calculated at the MRPL/MRL. Each individual source of uncertainty was combined and then expanded by a t distribution coverage factor (k) at a confidence level of 95.45%. Table 5 shows the uncertainty results for all analytes; the results are expressed as percentage at the MRPL/MRL. All uncertainty values were between 20% and 31%, which is completely acceptable for trace analysis methods. Conclusion The method developed in the present study enables quantitative determination of prohibited CAP and permitted FF drugs simultaneously and achieves the minimum limits required by national and international legislation. Satisfactory results obtained by submitting very different matrices such as milk and bovine muscle to the same procedure of extraction and clean-up indicated the possibility that the method should be applicable to the other matrices. The clean-up step is inevitable when analysing more than one compound in different matrices, and the choice of the MSPD technique proved one of the best options, being rapid, less manual-intensive and more eco-compatible. Acknowledgements The authors are grateful to CNPq for providing scholarship.

References Anado´n A, Martı´ nez MA, Martı´ nez M, Rı´ os A, Caballero V, Ares I, Martı´ nez-Larran˜aga MR. 2008. Plasma and tissue depletion of florfenicol and florfenicol-amine in chickens. J Agric Food Chem. 56:11049–11056. Antignac JP, Le Bizec B, Monteau F, Andre F. 2003. Validation of analytical methods based on mass spectrometric detection according to the ‘‘2002/657/EC’’ European decision: guideline and application. Anal Chem Acta. 483:325–334. Ashwin HM, Stead SL, Taylor JC, Startin JR, Richmond SF, Homer V, Bigwood T, Sharman M. 2005. Development and validation of screening and confirmatory methods for the detection of chloramphenicol and

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Food Additives and Contaminants Monitoring Program, Food and Drug Administration, Rockville, MD, Section 7303.039. Food and Drug Administration [FDA]. 2011. U.S. Government Printing Office 76, (56); 16291. Gantverg A, Shishani I, Hoffman M. 2003. Determination of chloramphenicol in animal tissues and urine liquid chromatography–tandem mass spectrometry versus gas chromatography–mass spectrometry. Anal Chim Acta. 483:125–135. Guy PA, Royer D, Mottier P, Gremaud E, Perisset A, Stadler RH. 2004. Quantitative determination of chloramphenicol in milk powders by isotope dilution liquid chromatography coupled to tandem mass spectrometry. J Chromatogr A. 1054:365–371. International Organization for Standardization [ISO]. 1997. ISO 11843-1:1997. Capability of Detection (Part 1): Terms and Definitions. Li J, Ding S, Zhang S, Li C, Li X, Liu H, Liu J, Shen J. 2006. Residue depletion of florfenicol and its metabolite florfenicol amine in swine tissues after intramuscular administration. J Agric Food Chem. 54:9614–9619. Lim JH, Kim MS, Hwang YH, Song IB, Park BK, Yun HI. 2010. Plasma and tissue depletion of florfenicol in olive flounder (Paralichthys olivaceus) after oral administration. Aquaculture. 307:71–74. Luo P, Chen X, Liang C, Kuang H, Lu L, Jiang Z. 2010. Simultaneous determination of thiamphenicol, florfenicol and florfenicol amine in swine muscle by liquid chromatography–tandem mass spectrometry with immunoaffinity chromatography clean-up. J. Chromatogr B. 878:207–212. Martins Juńior AH, Bustillos OV, Pires MAF. 2006. Determinaçaõ de resı´ duos de cloranfenicol em amostras de leite e mel industrializados utilizando a tećnica de espectrometria de massas em ‘‘tandem’’ (CLAE-EM/EM). Quim Nova. 29:586–592. Miller JN, Miller JC. 2005. Statistics and chemometrics for analytical chemistry. 5th ed. Harlow, Essex, England: Pearson Education. Ministe´rio da Agricultura Pecua´ria e Abastecimento [MAPA]. 2009. Instruçaõ Normativa N 24, de 14 de julho de 2009. Define os requisitos e crite´rios especı´ ficos para funcionamento dos Laborato´rios de Ana´lises de Resı´ duos e Contaminantes em Alimentos integrantes da Rede Nacional de Laborato´rios Agropecua´rios. Dia´rio Oficial da Uniaõ, 22 jul 2009. Seçaõ 1, p. 7. Ministe´rio da Agricultura Pecua´ria e Abastecimento [MAPA]. 2010. Instruçaõ Normativa n 8, de 29 de abril de 2010. Programa de Controle de Resı´ duos e Contaminantes em Carnes (Bovino, Aves, Suı´ na e Equina), Leite, Mel, Ovos e Pescado do exercı´ cio de 2010. Dia´rio Oficial da Uniaõ, 03 mai. 2010. Seçaõ 1, p. 27. Mottier P, Parisod V, Gremaud E, Guy PA, Stadler RH. 2003. Determination of the antibiotic chloramphenicol in meat and seafood products by liquid chromatography– electrospray ionization tandem mass spectrometry. J Chromatogr A. 994:75–84. Nicolich RS, Werneck-Barroso E, Marques MAS. 2006. Food safety evaluation: detection and confirmation of chloramphenicol in milk by high performance liquid chromatography-tandem mass spectrometry. Anal Chim Acta. 565:97–102.

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Peng L, Yueming Q, Huixia C, Ying K, Yingzhang T, Daning W, Mengxia X. 2006. Simultaneous determination of chloramphenicol, thiamphenicol, and florfenicol residues in animal tissues by gas chromatography/mass spectrometry. Chin J Chromatogr. 24(1):14–18. Pezza L, Rios A`, Nozal L, Arce L, Valca´rcel M. 2006. Simultaneous determination of chloramphenicol, thiamphenicol and florfenicol residues in bovine milk by micellar electrokinetic chromatography. Quı´ m Nova. 29(5):926–931. Pfenning AP, Madson MR, Roybal JE, Turnipseed SB, Gonzales SA, Hurlbut JA, Salmon GD. 1998. Simultaneous determination of chloramphenicol, florfenicol, and thiamphenicol residues in milk by gas chromatography with electron capture detection. J AOAC Int. 81:714–720. Pfenning AP, Roybal JE, Rupp HS, Turnipseed SB, Gonzales SA, Hurlbut JA. 2000. Simultaneous determination of residues of chloramphenicol, florfenicol, florfenicol amine, and thiamphenicol in shrimp tissue by gas chromatography with electron capture detection. J AOAC Int. 83:26–30. Ramos M, Mun˜hoz P, Aranda A, Rodriguez I, Diaz R, Blanca J. 2003. Determination of chloramphenicol residues in shrimps by liquid chromatography-mass spectrometry. J Chromatogr B. 791:31–38. Rodziewicz L, Zawadzka I. 2008. Rapid determination of chloramphenicol residues in milk powder by liquid chromatography–electrospray ionization tandem mass spectrometry. Talanta. 75:846–850. Ronning HT, Einarsen K, Asp TN. 2006. Determination of chloramphenicol residues in meat, seafood, egg, honey, milk, plasma and urine with liquid chromatographytandem mass spectrometry, and the validation of the method based on 2002/657/EC. J Chromatogr A. 1118:226–233. Schencka FJ, Calleryb PS. 1998. Chromatographic methods of analysis of antibiotics in milk. J Chromatogr A. 812:99–109. Scortichini G, Annunziata L, Haouet MN, Benedetti F, Krusteva I, Galarini R. 2005. Elisa qualitative screening of chloramphenicol in muscle, eggs, honey and milk: method validation according to the Commission Decision 2002/ 657/EC criteria. Anal Chim Acta. 535:43–48. Shen H, Jiang H. 2005. Screening, determination and confirmation of chloramphenicol in seafood, meat and honey using ELISA, HPLC-UVD, GC-ECD, GC-MS-EISIM and GC-MS-NCI-SIM methods. Anal Chim Acta. 535:33–41. Shen J, Xi X, Jiang H, Li C, Li J, Li X, Ding S. 2009. Determination of chloramphenicol, thiamphenicol, florfenicol, and florfenicol amine in poultry and porcine muscle and liver by gas chromatography-negative chemical ionization mass spectrometry. J Chromatogr B. 877:1523–1529. Shi X, Wu A, Zheng S, Li R, Zhang D. 2007. Molecularly imprinted polymer microspheres for solid-phase extraction of chloramphenicol residues in foods. J Chromatogr B. 850:24–30. Souza SVC, Junqueira RG. 2005. A procedure to assess linearity by ordinary least squares method. Anal Chim Acta. 552:25–35.

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Souza SVC, Pinto CT, Junqueira RG. 2007. In house method validation: application in arsenic analysis. J Food Comp Anal. 20:241–247. Van de Riet JM, Potter RA, Christie-Fougere M, Burns BG. 2003. Simultaneous determination of residues of chloramphenicol, thiamphenicol, florfenicol, and florfenicol amine in farmed aquatic species by liquid chromatography/mass spectrometry. J AOAC Int. 86:510–514. Woodward KN. 2004. Assessing the safety of veterinary drug residues. In: Watson DH, editor. Pesticide, veterinary and

other residues in food. Cambridge (England): Woodhead. Chapter 8, p. 189–191. Wu J, Chang C, Ding W, He D. 2008. Determination of florfenicol amine residues in animal edible tissues by an indirective competitive ELISA. J Agric Food Chem. 56:8261–8267. Zhang S, Liu Z, Guo X, Cheng L, Wang Z, Shen J. 2008. Simultaneous determination and confirmation of chloramphenicol, thiamphenicol, florfenicol and florfenicol amine in chicken muscle by liquid chromatography– tandem mass spectrometry. J Chromatogr B. 875:399–404.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 571–576

Producing a sulfamethazine quality control material under the framework of ISO/CD Guide 80 A.L. Cunha, P.F. Silva, E.A. Souza, J.R.A.M. Ju´nior, F.A. Santos and E.A. Vargas* Ministe´rio da Agricultura, Pecua´ria e Abastecimento – MAPA Laborato´rio Nacional Agropecua´rio de Minas Gerais – LANAGRO/MG, Av. Roˆmulo Joviano, s/n Cx. Postal 35, 50 CEP.: 33600-000 Pedro Leopoldo/Minas Gerais, Brazil (Received 29 November 2010; final version received 8 November 2011) The increasing use of antimicrobial agents such as sulfonamides by the pig industry is of concern, since residues in both pork and its by-products, when derived from animals treated improperly, can endanger human health. The aim of this study was to establish the production conditions and to evaluate the homogeneity and the stability of sulfamethazine in porcine liver quality control material, produced ‘in-house’ for use in ring tests of the laboratory network of residues and contaminants of the Ministry of Agriculture, Livestock and Food Supply, Brazil. In the process of preparing the material, a FOSS blender was used, where the samples were ground to obtain a homogeneous mass, which was packed in polypropylene bottles. The material resulting from this process of homogenisation was sampled and analysed by LC/MS/MS. The analytical results were statistically evaluated by one-way ANOVA. According to statistical evaluation, the material produced was considered homogeneous, with 95% confidence. Stability tests were performed with the bottles stored under the specified storage conditions. They were randomly selected and analysed in duplicate by the same analytical method as the homogeneity study. The analytical results were statistically evaluated by the procedures for a stability check described in ISO 13528:2005, indicating that the material was unstable under the conditions of storage. Keywords: chromatography – LC/MS; veterinary drug residues – sulphonamides; animal products – meat

Introduction The consolidation of pork meat production by Brazilian agribusiness is directly linked to technological improvements in production, reaching higher productivity rates, better production costs and product quality to meet the demands of the world market (Alves et al. 2002). In the context of international production and the export of pork, Brazil has played a relevant role and is currently fourth producer in the world ranking of production. Despite the impact of the global financial crisis, Brazilian pork meat production estimated in 2009 was around 2.9 million tons, reaching a level of export of around 607,000 tons (Associac¸a˜o Brasileira da Indu´stria Produtora e Exportadora de Carne Suı´ na (ABIPECS) 2010). Brazil, as a major producer and exporter of food, needs to ensure that its products comply with quality criteria required by consumers, since food safety is an extremely important factor for competition in the global market and to expand export trade. Food safety depends largely on the control of residues and contaminants that may be present in food as a result of production practices. Veterinary drugs belonging to the class of sulfonamide antimicrobial agents are used extensively in

animal production for disease control and, through synergistic action with other drugs, to promote animal weight gain. The presence of sulfonamides is regulated worldwide, including in Brazil, and the maximum residue limit (MRL) for sulfamethazine in porcine liver is 100 mg kg 1 according to the Codex Alimentarius Commission (2009). However, the inappropriate use of these drugs such as non-compliance with the withholding period, incorrect dosages at levels above those allowed by legislation, a non-recommended way of administration and indiscriminate therapy can leave residues in food of animal origin endangering human health (Maffei et al. 2009), and possibly also inhibiting export/import between countries (Sanches et al. 2003). According to the National Program for Control of Residues and Contaminants (PNCRC) of the Ministry of Agriculture, Livestock and Food Supply (MAPA), sulfamethazine was the veterinary product, belonging to the sulfonamide class, responsible for the largest number of non-compliant meat samples in 2008 (Brasil. Ministe´rio da Agricultura, Pecua´ria e Abastecimento 2009). Within the monitoring and quality control of animal products chain, the laboratory is one of the main agents responsible for ensuring food safety, validating the actions taken by the

*Corresponding author. Email: inter.lanagromg@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 MAPA http://dx.doi.org/10.1080/19440049.2011.641162 http://www.tandfonline.com

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surveillance and inspection services. Thus, the performance of its activities involves the use of validated analytical methods that produce reliable results, with method performance in compliance with criteria defined by international directives such as Commission Decision 2002/657/EC and the Codex Alimentarius. Having a traceable system in agriculture (i.e. in chemistry) is a difficult task due to the great number of matrix products that must be assessed and the new analytical problems that arise involving a variety of new chemicals being introduced in the production of food worldwide, and new environmental, chemical and biological contaminants of food. A laboratory network as a component of the National Program for Residues and Contaminants is fundamental to provide traceable and reliable data to regulate and control food production and to evaluate the risk exposure to residues and contaminants. Traceability at the laboratory level is a key aspect for traceability in the agricultural chain from the food safety perspective – it is a ‘farm-to-fork’ ideal concept. Because of the nature of the measurement in the agricultural field, the number of substances and matrix involved is complex and makes it difficult to built a hierarchical traceable system from chemical standard to analytical standard (pure or matrix substances). Appropriate certified reference materials (CRM) and reference materials (RM) are essential to improve the traceability system, but only a few are available worldwide. This laboratory has attempted to purchase CRMs and RMs available from proficiency testing scheme providers and to produce its own RM for quality control. Nevertheless, the designed value (x U) of these materials lacks in most cases traceability to a CRM. The demand for RMs has greatly increased in recent years due to the increasing importance of standardisation and the traceability of measurements. However, there is low market availability of RMs of complex matrices such as animal tissue and to produce RMs is a demanding activity, has a high cost, and involves requirements of organisation and management, as well as technical and production. Where no suitable CRM is available, laboratories may use a Quality Control Material (QCM). There is a recognition of the necessity of QCMs that are ‘materials or substances, whose property values are sufficiently homogeneous, stable and well established to be used for maintaining or monitoring measurement processes. A QCM does not have formally assigned property values or uncertainties’ (International Organization for Standardization (ISO) 2010). Such materials do not require characterisation by metrologically valid procedures, and can be prepared ‘inhouse’ by the laboratory for its own internal use (ISO 2010). The production of quality control materials is similar to the production of RMs, as described in ISO (1989, 2000). QCMs should comply with the basic

Table 1. Property values of samples of porcine liver. Analyte

Concentration (mg kg 1)

Sulfamethazine

102.2 21.2 154.0 113.8 115.8

Matrix Porcine liver

requirements of any RM; they must be sufficiently homogeneous and stable, but value assignment, the establishment of traceability, uncertainty determination and extensive stability testing are not required for this type of RM (ISO 2010). The aim of this study was to establish production conditions and to evaluate the homogeneity and stability of sulfamethazine in porcine liver quality control material, establishing general guidelines for the future production of RMs on specific areas of action of the National Agricultural Laboratory of Minas Gerais State – Lanagro/MG.

Materials and methods Material Five porcine liver materials incurred with sulfamethazine derived from the PNCRC had their property values established, as shown in Table 1. The samples were stored in a freezer at an average 22 2 C until the starting date of the preparation of the material.

Analytical method Sulfonamides were extracted from liver tissue by ethyl acetate, acidified with acetic acid purified in a prepacked cation exchange column (Varian Bond ElutSCX 500 mg, 3 ml) and eluted with ammonia (35%) in acetonitrile solution (5:95 v/v). The extract was concentrated and re-dissolved in methanol prior to quantification and confirmation by LC-MS/MS (source ESI). An Applied Biosystems LC-MS/MS system, an Agilent 1100 HPLC system and an API5000 triple quadrupole mass spectrometer were used. Chromatographic separation was carried out using a Lichrospher 100 RP-18 and 5 mm coupled to a Lichrospher 100 RP-18 pre column, using a solution of water (75%), acetonitrile (15%), methanol (10 %) and formic acid (0.1% v/v) as the mobile phase, with a flux rate of 1.0 ml min 1. The gradient of the mobile phase was isocratic. The total run time was 28 min; the injection volume was 20 ml. The validation of this method followed the criteria and requirements of Commission Decision 2002/657/ EC. The following parameters were evaluated in pig liver: linearity, working range, effect matrix, trueness, repeatability, intra-laboratory reproducibility, CC ,

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Table 2. Analytical results for the sampled bottles – homogeneity study. Analytical results (mg kg 1) Material

Identification of bottle

Replicate 1

Replicate 2

1 2 3 4 5 6 7 8 9 10

78.1 83.9 78.8 72.2 70.0 68.5 82.4 67.1 67.8 72.2

79.5 84.6 67.1 88.3 94.1 91.9 90.5 91.2 81.7 83.9

Sulfamethazine in porcine liver

CC and selectivity. The evaluation of repeatability of the method was through the extraction of 18 blank samples in the following concentration levels: six blank samples fortified at 50 mg kg 1 (0.50 MRL), six blank samples fortified at 100 mg kg 1 (1.00 MRL), six blank samples fortified at 150 mg kg 1 (1.50 MRL), and six blank samples fortified at concentration levels of 0, 50, 75, 100, 125 and 150 mg kg 1. This experiment was conducted on 3 different days. To evaluate the reproducibility, the same experiment described above was repeated, performed by another analyst. Values for CC and CC were determined from the combination of data from the standard deviation (SD) of blank samples fortified and the curves of limit prediction of the calibration curve obtained in 6 days of analysis (repeatability and reproducibility). LOD and LOQ were calculated by extrapolation of the curves of limit prediction. A sulphamethazine reference standard with purity of 99.8% was obtained from Riedel-de-Haen (Vetranal, Germany). Method performance in the range of 50–150 mg kg 1 was: CC ¼ 114.2 mg kg 1; CC ¼ 128.3 mg kg 1; LOD ¼ 11.7 mg kg 1; LOQ ¼ 15.9 mg kg 1; measurement expanded uncertainty ¼ 13.9 mg kg 1; precision (repeatability RSD: 5.7% and intra laboratory reproducibility RSD: 14.6%); and recovery (96.6–104.9%).

Average of the analytical results (mg kg 1)

Median of the analytical results (mg kg 1)

Standard deviation

Coefficient of variation

79.66

80.60

9.02

11.32

bottles, with a yield of 53 units each containing approximately 30 g.

Homogeneity tests Ten bottles were randomly taken and analysed in duplicate, according to the previously mentioned method of analysis, in order to evaluate the homogeneity of the material. A 5 g subsample used to assess the homogeneity. The analytical results were statistically evaluated by one-way ANOVA.

Stability study The homogeneous and packed material was stored at an average temperature of 22 2 C. Stability tests were performed at zero time and every 30 days during 2 months’ storage. During these specific time intervals, at least three bottles stored under the specified storage conditions were randomly selected and analysed in duplicate, by the same analyst, using the same analytical method and the same equipment of the homogeneity study. The analytical results were statistically evaluated by the procedures for a stability check described in ISO 13528:2005 (ISO 2005).

Results and discussion Material preparation For the preparation of the material (1800 g), samples were ground and mixed thoroughly for a predefined homogenisation time using a stainless steel Foss blender in order to obtain a slurry with uniform consistency. The resulting material from this process of homogenisation was packed into 50 ml polypropylene

The concentration (mg kg 1) and the descriptive statistical analysis related to the determination of sulfamethazine in 10 sampled bottles in the homogeneity study using are shown in Table 2. Sulfamethazine was shown to be normally distributed in the liver material, as indicated by the very close means and medians as well as demonstrated by the Anderson–Darling normality test (Figure 1 and Table 3). These results show that the material preparation conditions established

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were appropriate to produce homogeneous liver material. One-way ANOVA evaluates the hypothesis of sampling variance being equal to zero, indicating homogeneity, while sampling variances greater than zero indicates possible non-homogeneity. The decomposition of the extrinsic and intrinsic variances bottles, represented by the mean squares between and within bottles, is presented in Table 4. As shown in Table 4, no significant differences were found among the averages of the sampled bottles [F(0.27) 5 Fcritical(3.02)], with 95% confidence, thus indicating that the sulfamethazine in porcine liver material produced in-house was considered

homogeneous. The homogeneity of the QCM was established with the sample quantity of 5 g. The concentration (mg kg 1) and the descriptive statistical analysis related to the determination of sulfamethazine in the sampled bottles in the stability study are shown in Table 5. The results from the bottles analysed in homogeneity assessment were considered the time zero (T0). Time one (T1) and time two (T2) correspond to the analytical results of sampling bottles that were analysed after a period of storage of 30 and 60 days, respectively. The statistical analysis to check the stability of the material using ISO (2005) is presented in Table 6. Residual sulfamethazine in porcine liver material has been shown to be unstable, as indicated by differences in the averages between the storage times that were out of a range of 30% of the target standard deviation. The instability was similar to that described by Alfredsson and Ohlsson (1998), where the Table 5. Analytical results of the stability study. Storage time Parameter

Concentration of sulfamethazine (mg kg 1)

78.1 83.9 78.1 72.2 70.0 68.5 82.4 67.1 67.8 72.2 79.5 84.6 67.1 88.3 94.1 91.9 90.5 91.2 81.7 83.9

69.7 66.2 71.4 74.0 84.4 69.7 82.7 67.1 75.7 79.2

76.3 64.4 64.4 58.5 52.5 58.5 52.5 58.5 46.6 58.5 58.5 58.5 52.5 46.6 58.5 64.4

Average Standard deviation Coefficient of variation (%)

79.66 9.02 11.32

74.01 6.38 8.62

58.11 7.35 12.65

Figure 1. Andersonâ&#x20AC;&#x201C;Darling normality test graph.

Table 3. Andersonâ&#x20AC;&#x201C;Darling confidence.

normality

Analyte

test

with

95%

Sulfamethazine

Valor P p-value 4 0.05

0.2514 ACCEPT

Table 4. Sulfamethazine in porcine liver: homogeneity study â&#x20AC;&#x201C; one-way analysis of variance (ANOVA). Source of variation

Sum of squares

Degrees of freedom

Mean square

Between bottles Within bottles

302.135 1243.775

9 10

33.571 124.377

Total

1545.909

F-value

p-value

Critical F-value

0.270

0.969

3.020

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Table 6. Sulfamethazine in porcine liver: stability study – ISO 13528:2005.

Parameter Standard deviation (b ) ¼ 17.52 0:3b ¼ 5.26 Average of previous time (x) Average of next time (y) x y 0:3b jx yj

Comparison between T0 and T1

Comparison between T0 and T2

Comparison between T1 and T2

79.66 74.01 5.65 Unstable

79.66 58.11 21.55 Unstable

74.01 58.10 15.90 Unstable

Figure 2. Selected-ion monitoring of a porcine liver sample containing sulfamethazine: (m/z) 279 Da, and its metabolites N4desamino-sulfamethazine: (m/z) 261 Da, N4-acetyl-sulfamethazine: (m/z) 321 Da and N4-glucoronyl-sulfamethazine: (m/z) 477 Da.

sulfamethazine residues decreased during storage, even at 20 C. Parks (1984) observed that transformation of sulfamethazine to its conjugated N4-glucopyranosylmetabolite will occur even in spiked samples in the frozen condition and this is probably one reason for the decline of the parent drug during storage. According to the Food and Agriculture Organization (FAO) (JECFA 1990), residues of sulfamethazine in liver and muscle of pigs are converted either chemically or enzymatically to N-glucose-sulfamethazine during storage at 20 C. To verify the presence of sulfamethazine metabolites, the mass spectrum was obtained after 60 days of storage at an average temperature of 22 C. The mass spectrum confirmed the identity of the molecular mass to sulfamethazine (m/z): 279 Da; and its metabolites N4-desamino-sulfamethazine: (m/z) 261 Da; N4-acetylsulfamethazine: (m/z) 321 Da; N4-glucoronyl-sulfamethazine: (m/z) 477 Da, as shown in Figure 2. As the results obtained in the stability evaluation showed that the conditions for preparation and storage of quality control material were not sufficient to ensure the stability of sulfamethazine for a period of 2 months, further studies should be performed. In future it is necessary to refine the production process, probably by freeze drying or adding a stabiliser to

avoid the decrease in sulfamethazine concentrations. Another possibility would be to reduce the storage temperature. Crooks et al. (1996) have shown the effect of four different temperatures (20, 4, 20 and 70 C) on the stability of sulfamethazine in porcine liver. Only the vials stored at 70 C remained stable during the storage period of 12 months, with sulfamethazine concentration in a range of variation of 2 SD.

Conclusion Quality control materials produced ‘in house’ allows the simulation of the conditions present in samples routinely analysed in agricultural laboratories and it is of great importance for the use of the network of residues and contaminants from the Ministry of Agriculture, Livestock and Food Supply. ISO (2010) is foreseen as an important tool in order to demonstrate that a MAPA measurement system is under statistical control and performs as expected, providing reliable results. It can be successfully applied to the MAPA laboratory network as long as the laboratories participate in a proficiency test scheme allowing the better comparability of results. The homogeneity and stability study of sulfamethazine in porcine liver material allowed the establishment

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of initial guidelines for the production of QCM for sulfamethazine residues in incurred animal tissues. The results of this study indicate that the homogenisation procedure performed was appropriate; however, the storage temperature of 20 C was not sufficient to ensure the stabilisation of the analyte for a period of 2 months. Evidence for this instability was detected by the presence of metabolites identified by mass spectrometry. Aiming to overcome the losses of sulfamethazine in porcine liver quality control material during processing and storage, further studies should be performed to improve the production process, probably by freeze drying, adding a stabiliser or by reducing the storage temperature.

References Alfredsson G, Ohlsson A. 1998. Stability of sulphonamide drugs in meat during storage. Food Addit Contam. 15(3):302–306. Alves FJX, Silva TJPL, Faria VI. 2002. Ocorreˆncia de resı´ duos de sulfametazina em fı´ gado e mu´sculo de suı´ nos abatidos no Estado do Rio de Janeiro. Hig Aliment. 16(98):74–78. Associaçaõ Brasileira da Indu´stria Produtora e Exportadora de Carne Suı´ na (ABIPECS). 2010. Abipecs confirma exportaço˜es de 607,49 mil t em 2009; [cited 2010 Mar 2]. Available from: http://www.abipecs.org.br/pt/estatisticas/ mundial/exportaçaõ.html/ Brasil. Ministe´rio da Agricultura, Pecua´ria e Abastecimento. 2009. Instruçaõ Normativa n 15, de 25 de maio de 2009. Anexo II. Plano Nacional de Controle de Resı´ duos e Contaminantes. Codex Alimentarius Commission. 2009. Report of the Eighteenth Session of the Codex Committee on residues

of veterinary drugs in foods. Thirty-Second Session. Natal (Brazil): Codex Alimentarius Commission. Crooks SRH, McCaughey WJ, Elliott CT, McEvoy JD, Hewitt AS. 1996. The production of pig tissue sulphadimidine reference material. Food Addit Contam. 13(2):211–219. International Organization for Standardization (ISO). 1989. ISO Guide 35: certification of reference materials – general and statistical principles. 2nd ed. Geneva (Switzerland): ISO. International Organization for Standardization (ISO). 2000. ISO Guide 34: general requirements for the competence of reference material producers. Geneva (Switzerland): ISO. International Organization for Standardization (ISO). 2005. ISO 13528.2005 (E). Statistical methods for use in proficiency testing by interlaboratory comparisons. Geneva (Switzerland): ISO. International Organization for Standardization (ISO). 2010. ISO/CD Guide 80 (Draft) – Guidance for in-house production of reference materials for metrological quality control (QCMs). ISO/REMCO/WG 8. Geneva (Switzerland): ISO. JECFA. 1990. Residues of some veterinary drugs in animals and foods: monographs prepared by the Thirty-fourth Meeting of the Joint FAO/WHO Expert Committee on Food Additives. FAO Food and Nutrition Paper, no. 41/2. Rome (Italy): FAO. Maffei DF, Nogueira ARA, Brondi SHG. 2009. Determinac¸a˜o de resı´ duos de pesticidas em plasma bovino por cromatografia gasosa-espectrometria de massas. Quı´ mica Nova. 32(7):1713–1716. Parks OW. 1984. Evidence for transformation of sulphamethazine to its N4-glucopyranosyl-derivative in swine liver during frozen storage. J Assoc Off Analyt Chem. 67:566–569. Sanches SM, Silva CHTP, Campos SX, Vieira EM. 2003. Pesticidas. Resvista de Ecotoxicologia e Meio Ambiente. 13:53–58.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 577–586

Bioactivity-based screening methods for antibiotics residues: a comparative study of commercial and in-house developed kits Rodrigo Hoffab*, Fabiana Ribarckia, Ivomar Zancanaroa, Lara Castellanoa, Carolina Spiera, Fabiano Barretoac and Suzana Horta Fonsecaa a Ministe´rio da Agricultura, Pecua´ria e Abastecimento, Laborato´rio Nacional Agropecua´rio – LANAGRO/RS, Estrada da Ponta Grossa, 3036, CEP 91780-580, Porto Alegre, RS, Brazil; bInstituto de Quı´mica, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; cFaculdade de Farma´cia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil

(Received 26 November 2010; final version received 7 November 2011) Two bioactivity-based screening methods for antibiotic residue analysis (FAST Antimicrobial Screening Test and PremiÕ Test) were compared, in terms of sensitivity, with a new in-house developed tube test assay using Escherichia coli. Tests were performed using antibiotic standards, spiked samples and real incurred samples. The minimum inhibitory concentration (MIC) for several antibiotics was established and compared with maximum residue levels (MRLs) in samples. The results of all evaluated tests are compared with liquid chromatography–tandem mass spectrometry multi-residue screening tests to compare parameters such as sample preparation, cost, time of analysis and confidence in results. For all tests, values of half the maximum residue limit (0.5 MRL) were considered as a satisfactory target for a screening method. The potential and limitations of each method are discussed to indicate more rational and effective strategies for high-throughput residue monitoring and surveillance programmes. It was concluded that bioactivity-based screening methods are a useful tool, but the best compromise between minimum performance limits, cost and selectivity must be taken into account. For laboratories equipped with mass spectrometry, multi-class screening methods provide more specific responses with high sensitivity. Keywords: bioassay; in-house validation; microbiology; veterinary drug residues, antibiotics; animal products, meat

Introduction Food contaminants can originate from every step in the food chain and include environmental contaminants such as heavy metals, dioxins and pesticides. An important source of residues in food is via animal disease treatment and/or the prophylactic use of veterinary drugs. Maximum residues limits (MRLs) have been established for many of these compounds (Council Regulation 2377/90 1990; European Commission 1990). Producers must guarantee their food products are not contaminated by any veterinary medicinal product from the list of prohibited antimicrobials or that the levels of these materials are lower than the MRLs (Zvirdauskiene and Salomskiene 2007). For banned substances, such as chloramphenicol, minimum required performance levels (MRPLs) are assessed (Mauricio et al. 2009) and, to ensure consumer safety, regulatory authorities carry out extensive monitoring and surveillance programmes. For this purpose, chemical methods are extensively applied to detect and quantify residues in foods. *Corresponding author. Email: rodrigo.hoff@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.641508 http://www.tandfonline.com

However, these methods can be laborious and expensive and some techniques demand a very long sample preparation time (Kinsella et al. 2009). Within the area of veterinary drugs, antibiotics present more potential risks to public health regarding not only allergenic or toxic effects caused by residues in food but also by long-term residue exposure, which can lead to the development of bacterial resistance. As opposed to contaminants such as heavy metals or pesticides, antibiotics have an intrinsic activity against microorganisms that can be used to detect their presence in several tissues and matrices using inexpensive bioactivity-based screening methods based on simple inhibitory activity or more complex interactions between microorganisms and antibiotic molecules. The later is the case with tetracycline screening using bioluminescence (Korpela et al. 1998; Pikkemaat et al. 2010); however, the majority of methods are based on plate or tube tests for bacterial growth inhibition. These screening methods are based on visible perception of microorganism inhibition caused by the

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presence of a substance with bactericidal or bacteriostatic activity. This perception can be obtained by a halo around a control disc containing a determined antibiotic concentration placed in an agar plate surface. This methodology is called (multi-) plate assay. Other methods, using the colour change of a pH indicator in presence of bacterial growth, are referred to as tube tests. A recent review of (multi-) plate and tube test assays was published by Bovee and Pikkemaat (2009). In Brazil, the Ministry of Agriculture, Livestock and Food Supply is responsible for implementing the National Residues Control Plan (NRCP). In this Plan, some bioactivity-based screening methods are applied to detect specific antibiotic groups. The FAST Antimicrobial Screening Test (FAST) method was initially used in our laboratories to screen kidney samples. Briefly, this method uses a Muller–Hinton agar plate which is carpetted with Bacillus megaterium spores. A sterile swab dipped in the sample is placed on the inoculated agar plate along with a neomycin antibiotic sensitivity disc as a positive control. The test plate containing the test swab, agar side down, is incubated at 37 C for 12–24 h. The zone of inhibition for the positive control antibiotic sensitivity disc must be within the limits set for the disc used. If there is any inhibition of bacterial growth around the sample swab, the test is considered positive for antibiotic residues. Another tube test, also performed as part of the NRCP, consists of a commercial kit (PremiÕ Test; DSM nutritional products, The Netherlands). In this test, ampoules containing solid agar with Geobacillus stearothermophilus spores, a thermophilic bacterium sensitive to many antibiotics, and bromocresol purple indicator are used (Stead et al. 2009). Sample extracts or tissue fluid are placed on top of the agar and the ampoules were incubated at 60 C. Bacterial growth produces metabolic products that alter the pH of the medium and change the colour from purple to yellow. In positive samples, no bacterial growth occurs and, consequently, the indicator retains its original colour. Although of low cost and relatively simple to operate, both (multi-) plate and tube test assays have some disadvantages: . Non-specific responses. Once a test detects inhibition activity that depends of microorganism sensitivity only, i.e. for each microorganism, a range of antibiotics can be tested at different concentrations. Furthermore, a positive result in these test demands quantitative and confirmatory analysis. However, considering that quantitative and/or confirmatory methods are generally developed for specific groups, a positive sample in FAST or PremiÕ Test can be submitted to more than

one method, since the antibiotic class responsible for bacterial growth inhibition is unknown. A recent report discusses the results of a proficiency test in which 23 laboratories used screening methods for antibiotics, with a false negative rate of 73% for microbiological methods (Berendsen et al. 2011). . Several endogenous factors in tissues can produce false-positive results. . Although metabolites are considered in terms of MRL, some antibiotic metabolites show either an absence or decreasing activity against bacteria and can not be detected in bioactivity-based assays. This is the case of sulfonamides, for instance. . Several samples were found to contain mixtures of antibiotics. In such cases, LC–MS/MS provides a significant advantage for identification and quantification, as mixtures can be problematic to interpret with microbial inhibition screening assays. An ideal screening test should generate a positive response at a concentration level that ensures a low number of false-negative and is below the MRL. For verification purposes, based on European Community Commission Decision 2002/657/EC (European Commission 2002), the detection capability must be determined for qualitative assays. This parameter, called CC , is the concentration at which the number of false-negative results is less than or equal to 5%. An evaluation of intra-laboratory repeatability, specificity, selectivity and robustness of assay is also recommended. Recently, several papers were published with comparison between tube test and (multi-) plate tests and also reporting performance verification or complete validation for these methods. Recent work published by Schneider et al. (2009) deals with comparison of three distinct methods (FAST, PremiÕ Test and Kidney Inhibition Swab (KIS)). In this work, serum and beef kidney juice samples were analysed using the three methods and results confirmed by mass spectrometry. It was concluded that none of the three tests is likely to be a perfect match for the needs of a broad antibiotic sampling programme. LC–MS/MS provides a much more definitive method for detection and identification of antibiotic residues, according to authors. In another publication (Cantwell and O’Keeffe 2006), PremiÕ Test and One-Plate test were compared for antimicrobials screening in kidney. One-Plate test is very similar to FAST, but uses Bacillus subtilis as the sensitive microorganism. They used aqueous solutions and kidney fluid to compare the two methods. Considering only kidney fluid as approximating real samples, only eight of 17 tested antibiotics could be

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Table 1. MRL values for bovine and poultry kidney and muscle (European Commission 2010).

Analyte

MRL bovine kidney (mg kg 1)

MRL bovine muscle (mg kg 1)

MRL poultry kidney (mg kg 1)

MRL poultry muscle (mg kg 1)

SDZ OTC TC CTC OXO NALIDa FLU ENROb DIFLO NORa SARAa VANa AMP AMO PEN G PEN Va CEF ESTREP NEO ERI TYL

100 600 600 600 150 NE 1500 200 800 NE NE NE 50 50 50 NE 1000 1000 5000 200 100

100 100 100 100 100 NE 200 100 400 NE NE NE 50 50 50 NE 200 500 500 200 100

100 600 600 600 150 NE 1000 300 600 NE NE NE 50 50 50 25 1000 1000 5000 200 100

100 100 100 100 100 NE 400 100 300 NE NE NE 50 50 50 25 200 500 500 200 100

Notes: NE, not established. Non-specific MRL established: MRL for similar substance was assumed as target value. b ENRO MRL is the sum of ENRO and CIPRO. a

detected at concentrations below the MRL using PremiÕ Test and just three drugs (flumequine, chlortetracycline and doxycycline) could be detected below the MRL using the One-Plate test. Other more complex microbiology methods use more than one microorganism, sometimes at distinct pH values, to provide an antibiotic group-specific response. Besides the improvement in specificity, the method becomes relatively more laborious (Pikkemaat, Rapallini, et al. 2008; Althaus et al. 2009). Another approach consisted of performing a post-screening microbiological test for suspect samples analysed in a more generic assay, as in the One-Plate test (Pikkemaat, van Dijk, et al. 2008). In several cases, pre-treatment of samples must be performed to concentrate analytes and/or inactivate endogenous inhibitory compounds. Matrices such as milk or kidney fluid are generally pre-processed by gently heating to inactivate non-specific inhibitory substances and microflora (Pengov and Kirbis 2009). In the present work, FAST and PremiÕ Test performance were compared with an in-house developed tube test assay, using antibiotics standards, spiked samples and real incurred samples. Minimum inhibitory concentration (MIC) for several antibiotics were established and compared with MRL levels in samples. The results of all evaluated test were compared with liquid chromatography–tandem mass spectrometry multi-residue screening tests to compare

parameters such as sample preparation, cost, analysis time and confidence in results. For all experiments, values of half the MRL (0.5 MRL) were considered a satisfactory target for a screening method. MRL values were assumed according to data showed in Table 1 (European Commission 2010).

Materials and methods Reagents and materials Analytical standards of sulfadiazine (SDZ), chlortetracycline (CTC), tetracycline (TC), oxytetracycline (OTC), oxolinic acid (OXO), nalidixic acid (NALID), flumequine (FLU), ciprofloxacin (CIPRO), difloxacin (DIFLO), enrofloxacin (ENRO), norfloxacin (NOR), sarafloxacin (SARA), vancomycin (VAN), ampicillin (AMP), amoxicillin (AMO), penicillin G (PEN G), penicillin V (PEN V), cephalexin (CEF), streptomycin (ESTREP), neomycin (NEO), erythromycin (ERI) and tylosin (TYL) were purchased from Riedel–de-Haen (Buchs, Switzerland) or from Sigma-Aldrich (St. Louis, MO, USA) as a powder. Stock standard solutions of each compound were prepared by dissolving 10 mg of analytical standard in 10 ml of appropriate solvent (water for macrolides, aminoglicosides and -lactams, methanol for tetracyclines, sulfonamides and quinolones; methanol with some drops ( 2) of 1 M NaOH for fluorquinolones). Aliquots of each stock solution

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are diluted with water to obtain the desired concentrations and were stored in a refrigerator. Formic acid was obtained from J.T. Baker (Phillipsburg, NJ, USA); methanol and acetonitrile (HPLC grade) were purchased from Merck (Darmstadt, Germany). All water used was ultra-pure deionised water produced by a Milli-Q apparatus (Millipore, Bedford, MA, US). Methanol and acetonitrile of HPLC grade were from J.T. Baker. Mueller–Hinton agar and blank disks cartridges (CT0998B) was purchased from Oxoid. Domestic scanner used to visualise PremiÕ Test results was calibrated with Kodak Ektacolor Professional Paper Q-60R1 Colour Input.

Samples Blank samples of meat (muscle, liver and kidney) were obtained from the Brazilian Federal Inspection Services (SIF), the food national inspection service managed by Brazilian Ministry of Agriculture, collected in several slaughterhouses. Bovine meat produced in organic management systems were acquired in a local market (Porto Alegre, Brazil).

Instrumentation and conditions Liquid chromatography–mass spectrometry All LC–MS/MS experiments and sample preparation for this technique were performed according to multi-residue analysis routinely in our laboratory (Bittencourt et al. 2011). LC–ESI–MS/MS measurements were carried out using a Waters Alliance 2795 system coupled to a Quattro Micro triple quadrupole mass spectrometer from Micromass (Waters) with an electrospray source. Separation was achieved on a Symmetry C18 LC column (75 4.6 mm; 3.5 mm particle diameter) from Waters. A Phenomenex C18 (4.0 3.0 mm) was used as guard column. The flow-rate was 400 ml min 1 and the column temperature was set at 20 C. The gradient elution program with solvent A (aqueous solution 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid) was as follows: 98% A (5 min), 98–80% A (2 min), 80% A (3 min), 80–50% A (1 min), 50% A (4 min), 50–98% A (2 min), kept at 98% A for 17 min, returning to initial composition and held for 3 min to equilibrate the column. Mass condition optimisation was achieved on infusion injection at a flow-rate of 10 ml min 1. Each standard solution was prepared separately in methanol with formic acid 0.1% at 1 mg ml 1. Source block temperature was set at 120 C in positive ion mode with a capillary voltage of 3.0 kV. Nitrogen gas was used as the desolvation and nebuliser gas (N2) at a flow-rate of 400 and 50l h 1, respectively. Argon was used as

collision gas. Detection was operated in multiple reaction monitoring (MRM) mode. Instrument control and data processing were carried out via of Masslynx 4.1 software purchased from Micromass. FAST Agar plates, neomycin standard disks, Bacillus megaterium ATCC 9885 spore suspension and blank disks were prepared in-house. Spore solutions were prepared from a BHI (brain–heart infusion) broth with 1.5 106 UFC ml 1. Spore solutions were tested using Mueller–Hinton agar plates with bromocresol purple and dextrose. As positive control, neomycin disks containing 5 mg of antibiotic were used. These disks must produce an inhibition halo of between 20 and 26 mm. To prepare these disks, neomycin (Sigma) standard solution was prepared in water and diluted to obtain a solution of 25 mg ml 1. An aliquot of 200 ml of this solution applied to a blank disk provided 5 mg of neomycin per disk. PremiÕ Test PremiÕ Test complete kit was purchased from DSM (Gellen, The Netherlands). All test were performed according to the manufacturer’s instructions, except concentration and/or extraction protocols developed in our laboratory specifically for this work. Samples were reader in an ordinary scanner. In-house developed tube test As a secondary aim, some kits were developed and tested in our microbiological laboratory. The first test used Escherichia coli as a sensitive bacterium; the second test used Staphylococcus aureus or Salmonella typhimurium. These kits were prepared in ampoules similar to PremiÕ Test. In empty liquid chromatograph vials (1.5 ml) previously sterilised, 400 ml of Mueller–Hinton agar with bromocresol purple were placed, containing 1.5 106 cells ml 1 of E. coli or S. aureus. After cooling the agar, 100 ml of sample is pipetted directly onto the agar surface. These vials were then incubated at 37 C in a water bath for approximately 5 h. The end-point was the pH indicator colour change in the blank positive control (purple to yellow). The colour change is due to bacteria growth and the release of CO2. First, we tested the detection limit of each analyte in relation to each microorganisms. Briefly, 100 ml of an aqueous solution of each antibiotic were pipetted in a tube test. Concentrations equal to 25, 50 and 100% of the MRLs values were tested. To evaluate matrix effects, kidney juice from a blank sample was spiked with the desired analytes at the same range concentrations. For sample extraction, 10 g of homogenised kidney was centrifuged at 3000 g

Food Additives and Contaminants

Figure 1. FAST plate with control disk (N5) containing neomycin and two positives results (ENRO) for incurred sample with enrofloxacin.

for 10 min and the supernatant was removed and heated to 60 C for 15 min to promote enzyme and other non-specific inhibitor inactivation. The resulting extract was directly applied to the vials.

Results and discussion FAST (Fast Antimicrobial Screening Test) FAST is a plate assay that uses Bacillus megaterium (ATCC 9885) as the sensitive bacteria, incubated for 18 h at 44 C to provide optimum growth conditions (Dey et al. 1998). The appearance of an inhibition zone indicates the presence of antibiotic residues in the solution applied to a paper disc of 6 mm in diameter, as shown in Figure 1. The method consists of sowing a solution of spores of Bacillus in Petri dishes containing Hilton–Miller agar, Soon after, three blank disks are placed per plate containing a 5 -mg neomycin (N5) standard and, in the other two disks, 20 ml of pipetted juice extracted from the elected tissue, obtained by compression or homogenisation followed by centrifugation. The original method used a sterile swab to obtain the juice for the desired absorption in the matrix but, due to inaccuracy in juice volume absorbed by the swab, the procedure was modified. Also, the original method used a preliminary assessment after 6 h of incubation (the reason for the name ‘‘FAST’’, i.e. the speed of analysis), but difficulty in identifying the inhibition zone within this period, lead us to chose an evaluation after 18 h of incubation. MIC was determined by FAST plates prepared in-house. For this purpose, aqueous solutions of each analyte evaluated were directly applied to blank disks and, after the solvent was dry, disks were placed on the agar plates. The obtained MIC values were all higher than 0.5 MRL values (Table 2). Considering that the aqueous solution had no matrix effect and no recovery

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loss, compared to any other extraction procedure applied to extract tissue juice, FAST was unable to screening for the proposed analytes. Analytes with high MRL values (such as streptomycin) were not tested, since they were not essential for current purposes. MIC is determined as the antibiotic concentration necessary to produce an inhibition halo clearly distinct from negative control. The linear correlation for all tested analytes was satisfactory (>0.968), showing a linearity of responses for the range evaluated. Evaluation of blank samples was performed to assess matrix effects. A total of 20 bovine kidney samples were analysed by FAST. An inhibition zone was recorded in only five of 20 samples, with a mean halo diameter of 7.67 0.3 mm. In a recent paper (Schneider et al. 2008), which compared different techniques for screening antibiotic residues, including FAST, using kidney, the matrix effect was defined as an inhibition zone of 10 mm, with the sum of three standard deviations from the average value of negative samples (blank). MICs were determined for vancomycin (VAN), erythromycin (ERI), sarafloxacin (SARA), enrofloxacin (ENRO), ciprofloxacin (Cipro), norfloxacin (NOR), penicillin (PEN G), oxytetracycline (OTC), tetracycline (TC) and chlortetracycline (CTC). Only TC had a MIC value lower than the MRL, but above the desired level (0.5 MRL) for method detection (Table 2). The obtained MIC data were compared to data reported by Schneider et al. (2008). These authors used kidney matrix to evaluate the performance of FAST for penicillin G, sulfadimethoxine, oxytetracycline, tylosin, danofloxacin, streptomycin, neomycin and spectinomycin. Our findings regarding penicillin G and oxytetracycline, as tested in both studies, are in agreement (Table 3). These results can be best discussed by analysing Figure 2, in which the green bars show half the MRL values of each antibiotic, compared to values for spiked kidney juice (blue bars) and MIC values obtained with the aqueous solution (red bars). It can be concluded that FAST is unable to detect any of the tested substances at 0.5 MRL in spiked kidney juice or aqueous solutions (Table 4).

Naturally incurred samples To evaluate FAST’s capability to detect enrofloxacin in real samples, a farmed chicken was medicated with 300 mg l 1 Enrofloxacin BaytrilÕ (Bayer) in water over a period of 3 days. The animal was slaughtered on the fourth day. Samples of kidney show a positive result in FAST and were confirmed by LC–MS/MS (Figures 1 and 3), but it was not possible to correlate the

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Table 2. Results of minimum inhibitory concentration (MIC) determination. Concentration (ng per disk)

VAN

ERI

SARA

ENRO

CIPRO

NOR

DANO

OTC

CTC

15 25 30 45 50 60 75 100 150 175 200 225 r MIC (mg ml–1) Kidney MRL (mg g–1)

– – – – s s s s s s s s 0.9917 1.34 –a

– – s s s s s s s s s s 0.9680 0.44 0.2

– – – s s s s s s s s s 0.9936 1.45 0.2 b

– – s s s s s s s s s s 0.9910 1.38 0.2

– – s s s s s s s s s s 0.9963 1.57 0.2

– – – – – – – – s s s s 0.9969 6.42 0.2 b

– – – s s s s s s s s s 0.9961 0.48 0.05

– s s s s s s s s s s s 0.9763 1.08 0.6

s s s s s s s s s s s s 0.9822 0.48 0.6

– s s s s s s s s s s s 0.9870 0.91 0.6

Notes: s, sensitive. r ¼ linear correlation coefficient. a MRL not yet established for this matrix. b Non-specific MRL established: MRL for other fluorquinolones (ENRO and CIPRO) was assumed as target value.

Table 3. Comparison of minimum inhibitory concentrations (MIC) with literature data.

Analyte PEN OTC

MIC (mg ml 1)

MIC (mg ml 1) (Schneider et al. 2008)

Kidney MRL (mg g 1)

0.48 1.08

0.4 1.5

0.05 0.6

Figure 2. (Colour online). Comparison between spiked kidney juice MIC (blue/medium shade bars), aqueous solution MIC (red/dark shade bars) and 0.5 MRL for kidney (green/light shade bars). Units are given in mg ml 1.

by centrifugation. Acetonitrile was added to promote protein precipitation. After another centrifugation step, the supernatant was diluted with mobile phase and injected into the LC–MS/MS system. Quantitative analysis was performed using a matrix-matched calibration curve. Figure 3 shows the extracted chromatograms for ENRO (9.78 mg ml 1) and its metabolite CIPRO (180.8 mg ml 1). One alternative to improve FAST’s sensitivity was to try concentrating samples to increase detection capability. Thus, two extraction solvents were tested: acetonitrile and methanol. Briefly, tissues were crushed with an Ultra-Turrax disruptor and 5 ml of solvent was added. Then, the samples were centrifuged and the supernatant evaporated to dryness. Pellet was re-suspended in distilled water and this final extract was then pipetted onto blank disks. The results are presented in Table 5. Considering a halo >8 mm as a positive response, the acetonitrile blank sample showed a false-positive response, demonstrating that the solvent is capable of inhibiting growth of B. megaterium, leading to false positives. For methanol, all results were negative, including the spiked samples, which lead us to conclude that this solvent is not an effective choice and can produce false negative results.

PremiÕ Test inhibition zone obtained with the ENRO concentration due to very high tissue residue levels. For LC–MS/MS confirmation, kidney and muscle samples were analysed according to a method described elsewhere (Bittencourt et al. 2011). Briefly, samples were dispersed in sand and the liquid fraction was isolated

The test principle is based on inhibiting the growth of Geobacillus stearothermophilus, a thermophilic bacterium very sensitive to many antibiotics and sulfonamides. When ampoules containing the PremiÕ Test are heated at 64 C, spores germinate resulting in bacterial multiplication and a consequent increase in acid

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Table 4. Comparison between spiked kidney juice MIC, aqueous solution MIC and 0.5 MRL for kidney (units are in mg ml 1). Concentration

ERI

SARA

ENRO

CIPRO

PEN G

OTC

CTC

Spiked juice MIC (mg ml 1) Aqueous solution MIC (mg ml 1) Kidney 0.5 MRL (mg g 1)

0.34 0.44 0.1

0.78 1.45 0.1

0.76 1.38 0.1

1.21 1.57 0.1

0.53 0.48 0.025

1.83 1.08 0.3

0.60 0.48 0.3

0.48 0.91 0.3

Table 5. Halo diameter (mm) for spiked samples extracted with acetonitrile. Enrofloxacin 100 ng g 1

Tetracycline 300 ng g 1

Samples

Halo (mm)

Samples

Halo (mm)

>23 >23 >23 >23 >22 >22 >22 >22 >8 >8

1 2 3 4 5 6 7 8 Blank Acetic acid

>22 >22 >23 >23 >22 >22 >24 >24 >8 >8

1 2 3 4 5 6 7 8 Blank Acetic acid

Figure 3. Chromatogram of poultry kidney juice (incurred sample) with enrofloxacin at 9.78 mg ml 1 and ciprofloxacin at 180.8 mg ml 1.

production from Geobacillus metabolism, occurring only when no inhibitory substance is present. This is visible by a colour change (purple to yellow) in the indicator, bromocresol purple, added to the solid agar medium. When antibacterial compounds are present above the limit of detection (LOD), no growth occurs and the colour remains purple. Aiming to improve method sensitivity by sample concentration, improved protocols suggested by the manufacturer were tested. A extraction with acetonitrile/acetone (70:30, v/v) was performed. Briefly, 2 0.1 g of sample (muscle or kidney) was homogenised, extracted with organic solvent, centrifuged and the supernatant evaporated in a water-bath at 40–45 C

under a gently flow of nitrogen. The dry residue was reconstituted in sterile BHI broth (200 ml) and applied to the ampoules. Kidney, muscle and liver of cattle and swine were analysed using the PremiÕ Test. Preliminary results were unsatisfactory, since all results were positive. This find lead us to consider solvent interference, which caused a false-positive response. However, as these samples, assumed to be blank samples, were not analysed for a complete antibiotics profile (just for sulfonamides, tetracyclines, fluorquinolones and quinolones), no conclusions could be drawn. Indeed, the concentration protocol adds several steps, such as extraction, homogenisation, centrifugation and evaporation under nitrogen. This increases analysis complexity, taking 3–5 h just to prepare samples prior to testing (this already takes 3.5 h for incubation and colour reading). Changes in colour were sometimes difficult to interpret, which may be reflected in the number of false positive results obtained. In addition, incubation time for the PremiÕ Test appeared to be important, as continued incubation at 64 C past the time at which a negative control turned yellow could lead, eventually, to positive samples turning yellow (negative) as well. To avoid subjective interpretation by visual inspection, results were obtained using a scanner coupled with DSM interpretation software, which gives an assigned numerical z-value for each colour based in an

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Table 6. Comparative data for blank samples obtained from visual and scanner analysis. The z-value is a value given by the DSM software that use an algorithm based on negative values (<0.0) for positive results and positive numbers (>0.0) for negative samples. Description

z-value

Scanner

Visual

A1 B1 C1 D1 E1 E2

2.27 2.23 2.31 5.89 9.91 0.28

NEG POS POS POS POS NEG

POS POS POS POS POS NEG

Bovine liver Bovine kidney Bovine liver Swine kidney Poultry kidney Blank

algorithm. Values above zero indicate a negative result. Blank samples of bovine, poultry and swine liver and/or kidney, previously analysed by LC–MS/MS methods for sulfonamides, tetracyclines, fluorquinolones and quinolones, were applied to test several false positive results (Table 6). Again, an unknown inhibitor substance present in the samples (endogenous or exogenous) could be the reason. Cantwell and O’Keeffe (2006) found that the PremiÕ Test was rugged with respect to species, ampoule age and ampoule batch. In disagreement with this work, we found that the PremiÕ Test and FAST showed a high species and tissue dependence, varying performance between species and even between distinct tissues within the same species.

In-house developed tube tests E. coli tube test The E. coli tube test was designed to screen fluorquinolones in kidney. For this purpose, eight analytes were tested (NOR, CIPRO, ENRO, NALID, OXO, FLU, DIFLO and SARA). Considering just aqueous solutions from each fluorquinolone, E. coli was able to detect CIPRO and ENRO at a concentration of 0.25 MRL. NOR, NALID, OXO, FLU, DIFLO and SARA produced positive results at a concentration of 0.5 MRL. When spiked kidney was analysed, the results show a strong matrix effect. Here, the influence of agar pH was evaluated. The medium was tested at three different pH values (6.0, 7.0 and 8.0). Enrofloxacin and ciprofloxacin were correctly detected in bovine kidney and muscle at pH 7.0. Difloxacin was positive at pH 6.0 and 7.0. The other analytes, although detected when in solution, were not detected when in the matrix. To test specificity, eight vials were incubated with an aqueous solution of sulfadiazine, penicillin G, tetracycline, amoxicillin, neomycin, streptomycin and erythromycin at concentrations of 0.5, 1.0 and

1.5 MRL. All results were negative, with the exception of neomycin, which was detected at all tested concentrations. S. aureus tube test The tube test with S. aureus was developed to deal with other classes of antibiotics. Drugs tested were -lactams (ampicillin, amoxicillin, penicillin G, penicillin V, cephalexin), aminoglycosides (streptomycin and neomycin), macrolides (erythromycin and tylosin), tetracyclines (tetracycline) and sulfonamides (sulfadiazine). For LOD determination using separate aqueous solution of each antibiotic, S. aureus was capable of detecting at the 0.5 MRL level in bovine kidney for all analytes tested. However, when tested in a spiked matrix of bovine kidney, it was unable to detect any antibiotic at any of the three deferent pH values. Despite the low cost and speed of preparation and analysis, both in-house-developed tube tests had some disadvantages. Firstly, as E. coli and S. aureus do not produce spores, the tube tests have a short shelf-life and must be used within 48 h after preparation, preferentially. Vials must be maintained under refrigeration until the moment of analysis. Another serious drawback was observed when we tested other matrices or other species. For milk, results were negative for all testes substances. For poultry muscle and kidney, the tube tests did not reproduce the same results obtained for bovine muscle and kidney.

Conclusions Screening kits improve a laboratory’s operational capability with reduced costs. For a large number of samples, only positive samples are submitted to specific confirmation analysis. There are different kit methodologies for food analysis, which are specific for certain veterinary drugs or for multi-class analysis. These kits should be used according to laboratory demands and must be precise. The objective of this paper was to compare the performance of various kits available on the market for veterinary drugs analysis in edible tissues and to evaluate the possibility of developing screening methods ‘‘in loco’’ for the same purpose. The results, associated with kit costs, will serve to select the best method to be implemented and validated in MAPA laboratories. A general comparison between all discussed methods are presented in Table 7. In this study, we evaluated two well-established methods based on the bioactivity of antibiotics: FAST and PremiÕ Test. A new, in-house-developed method using E. coli or S. aureus as sensitive microorganism was also presented. LC–MS/MS multi-residue and multi-class analysis was also performed and compared with the bioactivity-based screening methods. In contrast to some literature reports, we found that

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Table 7. General comparison of bioactiviy-based screening methods and LC–MS/MS.

Criteria Sample preparation (time and difficult/easy)

Time of analisys Costs (low, medium or high)

Results confidence

FAST

Premi-Test

20 min for sample preparation; requires asseptic work and microbiology expertise Results in 18 h Sample preparation – medium Analytical system – not required Non-specific, non-selective

2 h for sample preparation; easily performed

Results interpretation

Visual inspection, subjective

General

Complex and laborious (spores suspension standardisation and process control)

Results in 6 h Sample preparation – high Analytical system – not required Non-specific, medium selectivity if use inhibitors (PABA or -lactamase) Visual inspection or optical lecture (scanner), subjective Commercial available

the MIC values for several target analytes were above the MRL values (Table 1). Thus, the screening method is discarded if it is not fit-for-purpose or concentration steps (solid–liquid extraction, organic solvent extraction and posterior concentration) are applied to achieve adequate detection capability. False-positive results were found in a high number of cases for all bioactivity-based methods and several causes were identified. Physiological pH differences between species, endogenous inhibitory substances and the matrix were found to be major causes. In comparison with LC–MS/MS screening methods, bioactivity-based methods are less expensive in terms of instrumental techniques. However, considering that every positive sample in a microbiological test must be analysed by a confirmatory methods, the use of such methods that can provide conclusive results, even for screening, may be the most rational choice. For laboratories that already have LC–MS/MS systems installed, direct analysis in these systems eliminates most false-positive results, permits metabolite identification, and provides qualitative results. Moreover, LC–MS/MS screening methods can be converted into semi-quantitative or quantitative methods. Notwithstanding, for antibiotics with high MRL values, such as aminoglycosides and some macrolides, bioactivity-based methods seems to be an appropriate approach, since these classes of antibiotics can be easily detected at values below the MRL, even without any pre-concentration strategy. This does not eliminate the high potential for

E. coli/S. aureus tube test

LC–MS/MS multiclass

2 h for sample preparation; requires asseptic work and microbiology expertise Results in 4 h Sample preparation – low Analytical system – not required Non-specific, nonselective

1 h for a batch preparation; easily performed

Visual inspection or optical lecture (scanner), subjective Low stability

15 min for each run Sample preparation – low Analytical system – high High selectivity and specificity

Confirmatory and objective Requires high technology and specialised technicians

false-positive results. For large scale monitoring programmes, fast responses and high confidence levels can be obtained using rapid screening methods based on mass spectrometry, followed by quantification of analytes via class-specific methods. Currently, this is the strategy applied in our laboratory for routine analysis in the National Residues and Contaminants Control Plan. References Althaus R, Berruga MI, Montero A, Roca M, Molina MP. 2009. Evaluation of a microbiological multi-residue system on the detection of antibacterial substances in ewe milk. Anal Chim Acta. 632:156–162. Berendsen BJA, Pikkemaat MG, Stolker LAM. 2011. Are antibiotic screening approaches sufficiently adequate? A proficiency test. Anal Chim Acta. 685:170–175. Bittencourt MS, Martins MT, de Albuquerque FGS, Barreto F, Hoff R. 2011. High-throughput multi-class screening method for antibiotic residue analysis in meat using liquid chromatography–tandem mass spectrometry: a novel minimum sample preparation procedure. Food Addit Contam A. DOI:10.1080/19440049.2011.606228. Boove TFH, Pikkemaat MG. 2009. Bioactivity-based screening of antibiotics and hormones. J Chromatogr A. 1216:8035–8050. Cantwell H, O’Keeffe M. 2006. Evaluation of the PremiÕ Test and comparison with the One-Plate Test for the detection of antimicrobials in kidney. Food Addit Contam A. 23(2):120–125.

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Dey BP, White CA, Reamer RH, Thaker NH. 1998. Detection of antimicrobial residues in meat and poultry tissue by screen tests. In: USDA/FSIS Microbiology laboratory guidebook. Chap. 33. 3rd ed. Washington (DC): United States Department of Agriculture. p. 33-1–33-57. European Commission. 1990. Council Regulation (EEC) No. 2377/90 of 26 June 1990: laying down a Community procedure for the establishment of maximum residue limits of veterinary medicinal products in foodstuffs of animal origin. Off J Eur Commun. L224:1–8. European Commission. 2002. Commission Decision 2002/657/EC of 12 August 2002: implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off J Eur Commun. L221:8–36. European Commission. 2010. Commission Regulation 37/2010 of 22 December 2009: on pharmacologically active substances and their classification regarding maximum residue limits in foodstuffs of animal origin. Off J Eur Commun. L15:1–72. Kinsella B, O’Mahony J, Malone E, Moloney M, Cantwell H, Furey A, Danaher M. 2009. Current trends in sample preparation for growth promoter and veterinary drug residue analysis. J Chromatogr A. 1216:7977–8015. Korpela MT, Kurittu JS, Karvinen JT, Karp MT. 1998. A recombinant Escherichia coli sensor strain for the detection of tetracyclines. Anal Chem. 70. 4457–4462. Mauricio AQ, Lins ES, Alvarenga MB. 2009. A national residue control plan from the analytical perspective. The Brazilian case. Anal Chim Acta. 637(1–2):333–336.

Pengov A, Kirbis A. 2009. Risks of antibiotic residues in milk following intramammary and intramuscular treatments in dairy sheep. Anal Chim Acta. 637:13–17. Pikkemaat MG, Rapallini ML, Karp MT, Elferink JWA. 2010. Application of a luminescent bacterial biosensor for the detection of tetracyclines in routine analysis of poultry muscle samples. Food Addit Contam A. 27(8):1112–1117. Pikkemaat MG, Rapallini ML, van Dijk SO, Elferink JWA. 2008. Comparison of three microbial screening methods for antibiotics using routine monitoring samples. Anal Chim Acta. 637:298–304. Pikkemaat MG, van Dijk SO, Schouten J, Rapallini ML, Kortenhoeven L, van Egmond HJ. 2008. Nouws antibiotic test: validation of a post-screening method for antibiotic residues in kidney. Food Control. 20:771–777. Schneider MJ, Lehotay SJ. 2008. A comparison of the FAST, Premi and KIS tests for screening antibiotic residues in beef kidney juice and serum. Anal Bioanal Chem. 390:1775–1779. Schneider MJ, Mastovska K, Lehotay SJ, Lightfield A, Kinsella B, Shultz C. 2009. Comparison of screening methods for antibiotics in beef kidney juice and serum. Anal Chim Acta. 637:290–297. Stead SL, Sharman M, Stark J, Geijp EML. 2009. Improvements to the screening of antimicrobial drug residues in food by the use of the PremiTestÕ . J Chromatogr A. 1216:8035–8050. Zvirdauskiene R, Salomskiene J. 2007. An evaluation of different microbial and rapid tests for determining inhibitors in milk. Food Control. 18:541–547.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 587–595

Optimisation and validation of a quantitative and confirmatory method for residues of macrolide antibiotics and lincomycin in kidney by liquid chromatography coupled to mass spectrometry C.P. Rezende*, L.F. Souza, M.P. Almeida, P.G. Dias, M.H. Diniz and J.C. Garcia Ministry of Agriculture, Livestock and Food Suply – MAPA, Laborato´rio Nacional Agropecua´rio – LANAGRO/MG, Brazil (Received 22 November 2010; final version received 16 December 2011) A solid phase extraction followed by a liquid chromatography (LC)-tandem mass spectrometry (MS/MS) detection method for the confirmatory analysis of lincomycin (LIN), clindamycin (CLI), tilmicosin (TIM), erythromycin (ERI) and tylosin (TYL) residues in kidney were optimised and validated for monitoring and controlling the use of these antibiotics in food producing-animals. The method optimisation was carried out by testing changes in the extraction buffer pH and in the ammonium/acetonitrile concentrations on SPE eluent solutions. The optimised extraction procedure involved the extraction of the analytes with a pH 8 phosphate buffer, clean-up on a reversed-phase mixed-cation exchange cartridge, followed by the elution of the analytes in a 98:2 acetonitrile/ammonia solution, concentration in air flow and re-dissolved with an 1:1 methanol/water solution. The analytes were detected in an LC-MS/MS system in electrospray positive ionisation mode. The validation was performed according to the European Commission Decision 2002/657/EC. Linearity was established for all analytes using the method of least weighted squares and CC values ranged from 5.3% to 21.1% higher than the minimum residue limit (MRL) values. The addition levels varied from 0.5 to 1.50 MRL for all analytes, with recoveries exceeding 92.5%. The relative standard deviations (RSD%) in terms of repeatability (n ¼ 54) and reproducibility (n ¼ 108) for all analytes were less than 21.6% and 21.4%, respectively. The uncertainties were calculated by simplified methods using the calibration curve uncertainty and the intermediate precision to obtain the combined measurement uncertainty. The results of the validation process demonstrated that this method is suitable for the quantification and confirmation of antibiotic residues for the Brazilian Residue and Contaminant Control Plan (PNCR). Keywords: macrolides; kidney; lincomycin; validation; LC-MS/MS; SPE

Introduction Methods for the quantification and confirmation of residues are very useful tools to ensure the safety of animal products consumed domestically and in demanding foreign markets all over the world. Thus, monitoring antibiotic residues in the food supply chain plays an important role in the field of veterinary medicine. Macrolides are broad-spectrum antibiotics widely used in veterinary medicine for the treatment of respiratory and enteric infections in cattle, sheep, pigs and poultry. These compounds are effective against gram-positive and some gram-negative bacteria, as well as against members of the group of Chlamydia (Berrada et al. 2007; McGlinchey et al. 2008). Lincomycin is an antibiotic of the lincosamide group used to control certain gram-positive bacteria and exerts its antibacterial action by inhibiting RNA-dependent protein synthesis by acting on the 50 S subunit of the ribosome, used in monopreparations and combined with other antibiotics

such as spectinomycin, sulfadimidine and gentamicin, orally, intra-muscularly or sometimes in the feed or drinking water (EMEA 2008). Incorrect use of these antibiotics may leave residues in edible tissues, causing toxic effects to consumers such as allergic reactions, or problems due to the induction of resistant strains of bacteria (Moats 1996 cited by Draisci et al. 2001). The chemical structures of some macrolides are shown in Figure 1 (Codony et al. 2002). There are several detection methods for macrolides and lincomycin in animal tissues. Microbiological assays are used to screen samples, although they are time-consuming, with poor specificity and selectivity, leading to false-positive results (Granelli et al. 2009). More specific methods are used, normally LC methods in combination with many kinds of detectors like UV, fluorometric, chemiluminescent and electrochemical. The increasing use of highly selective techniques such as mass spectrometry (MS), tandem mass spectrometry (MS/MS) (Adams et al. 2009) and time-of-flight mass spectrometry (TOF/MS) (Peters et al. 2009), coupled with advances in chromatographic technology,

*Corresponding author. Email: cristiana.rezende@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 MAPA – BRASIL http://dx.doi.org/10.1080/19440049.2011.652196 http://www.tandfonline.com

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Figure 1. Chemical structures of macrolides: clindamycin, erythromycin, lincomycin, tylosin and tilmicosin.

have made it possible to develop multi-residue methodologies covering many trace contaminants (Stubbings and Bigwood 2009). MS/MS has the advantage of simpler extraction procedures, higher sensitivity and efficiency (Draisci et al. 2001), although the complexity of tissue matrices in terms of content and analyte-tissue interactions must be considered in the development and optimisation of methods for the analysis of veterinary drug residues. Extracting antibiotics from kidney and liver is a critical step in this method because of the interferences caused by the high protein and fat content of these matrices. The matrix effect is also important in antibiotic recoveries. Macrolide extraction involves protein and fat removal by organic solvents and preconcentration in SPE cartridges (Berrada et al. 2007). A simple method for both confirmatory and quantitative multi-residue analysis of lincomycin (LIN), clindamycin (CLI), tilmicosin (TIM), erythromycin (ERI) and tylosin (TYL) residues in bovine kidney samples has been optimised and validated for the Brazilian Residue and Contaminant Control Plan (PNCR). The antibiotics have been extracted with a pH 8.0 phosphate buffer, clean-up on reversed-phase mixed-cation exchange and detection/quantification by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Method optimisation has been carried out by single comparisons among the original and optimised extraction steps. Instrumental parameters

have been validated according to the European Union Commission Decision 2002/657/EC with a subsequent extension of the validation for bovine, horse and chicken matrices, by evaluating the matrix effects, linearity, CC and CC intercomparison for each species. The measurement uncertainty of the method has been estimated from the linearity and precision data using a simplified methodology in accordance with the requirements of ISO/IEC 17025:2005.

Material and methods Reagents and chemicals Tylosin tartrate (mixed isomers), clindamycin hydrochloride, lincomycin hydrochloride, tilmicosin and erythromycin with a minimum purity of 99.5% were obtained from Sigma (St Louis, MO, USA). Acetonitrile, methanol and hexane were HPLC grade and supplied by Tedia (Farfield, USA). Formic acid (98%) was mass spectrometry grade and supplied by Fluka. Potassium dihydrogen phosphate (KH2PO4) and mono-hydrogen potassium phosphate (K2HPO4) were reagent grade and supplied by Sigma (St Louis, MO, USA). Ammonium hydroxide solution (30%) was reagent grade and obtained from Vetec (Rio de Janeiro, Brazil). All water was filtered in a Milli-Q Gradient system (Millipore Corporation, Billerica, MA, USA).

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Table 1. MS/MS parameters for the determination of LIN, CLI, ERI, TIM and TYL. Analyte

Precursor ion (m/z)

Product ions (m/z)

Cone voltage (V)

Collision energy (eV)

Dwell time (s)

Lincomycin

407.5

869.6

Clindamycin

425.5

Erythromycin

734.6

Tylosin

916.6

23 19 40 42 40 27 22 27 17 40 27

0.05

Tilmicosin

126 359 132 174 696 126 377 158 576 174 772

35 25 30 45

0.05 0.05 0.05 0.05

Note: Subscribed ion is the quantitative ion.

Preparation of standard solutions Individual stock solutions were prepared with methanol at concentrations of 100 mg mL 1 and stored at < 11 C, in the dark. Mixed work standard solutions for spiking with 15 mg mL 1 lincomycin, 10 mg mL 1 tilmicosin, 2 mg mL 1 clindamycin and erythromycin, and 1 mg mL 1 tylosin were prepared using appropriate dilution of the stock solutions with methanol and stored in the freezer at < 11 C in the dark. The solutions have remained stable for 6 months.

Apparatus Initial tests in the buffer pH of the extraction solutions have been performed in a Varian LC 1200 LC-MS/MS with single-piston pumps, model ProStar 210; automatic injector ProStar 410; mass spectrometer, model 1200 L; software, Varian Workstation 6.4.2 (CA, USA). Initially the work was developed at Varian L1200. For reasons of availability, validation was performed on Waters Quattro Premier XE (Waters, Manchester, UK). The validation studies were conducted with an electrospray ionisation source and run using a MassLynx 4.1 software. An Alliance 2695 LC system was used for chromatography. The LC column used was a Phenomenex Luna C18 (2), 100 mm 2 mm, 3 mm 1A. SPE clean-up step was carried out using a 24-place vacuum manifold for solid-phase extraction (SPE) and Bond Elut LRC Certify (200 mg, 3 mL) solid-phase extraction cartridges supplied by Varian (CA, USA). Sample dryness was measured using a Yamato water bath, model BT25.

LC-MS/MS conditions Chromatography was performed using a binary mobile phase with gradient elution. Solvent A was acetonitrile/ water 5:95, formic acid (0.1%) mixture and solvent B

acetonitrile/water 95:5, with formic acid (0.1%) mixture. The initial gradient composition was 100% A to 60% in 10 min and then returned to the initial composition in 12 min, stayed until 18 min. The flow rate was 0.3 mL min 1, and the column temperature was 30 C. The injection volume was 10 mL and the compounds studied were eluted within 9 min. Detection was carried out using electrospray ionisation in positive mode with multiple reaction monitoring (MRM), capillary voltage of 3.2 kV, source temperature of 120 C, desolvation temperature of 300 C and cone and desolvation gas flow rates of 50 and 700 L h 1, respectively. The transitions and collision energies used are shown in Table 1.

Maximum Residue Limits (MRLs) For bovines, the maximum residue limits (MRLs) in kidneys were set in 1500 mg kg 1 (lincomycin), 1000 mg kg 1 (tilmicosin), 200 mg kg 1 (erythomycin) and 100 mg kg 1 (tylosin) (ECC 1990; CAC 2003). The clindamycin MRL has not been established yet. Thus, the erythromycin MRL value of 200 mg kg 1 was adopted instead.

Sample extraction and cleanup The following procedure refers to the optimised method: Kidney samples of 2.00 0.10 g were weighed into 50-mL polypropylene centrifuge tubes. Six samples were spiked before the extraction with mixed work standard solutions to prepare matrix-fortified calibration standards equivalent to 0.5 to 1.5 MRLs, and six samples were spiked after the extraction to result in matrix-matched calibration standards at the same concentration levels. Ten milliliters of pH 8.00 phosphate buffer (0.2 mol L 1) were added to each tube, stirred in a horizontal mechanical shaker for 5 min and placed into an ultrasonic bath for 15 min, centrifuged

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at 4000 rpm for 30 min at approximately 4 C. The extract was loaded onto a solid-phase extraction cartridge previously conditioned with 5 mL of methanol and 5 mL of phosphate buffer, and gentle vacuum was used to start the elution with a slow flow, discarding the eluate. The cartridge was washed with 2 mL of HPLC-grade hexane, vacuum dried for 2 min or dried with 10 mL of air using a syringe. The analytes were eluted with 5 mL of an acetonitrile/ammonia (98:2) solution to a 10-mL centrifuge glass tube and evaporated to dryness under nitrogen or compressed air in a water bath at a temperature not exceeding 45 C. The dried extract was re-constituted with 1 mL methanol/water mixture at 50:50, agitated for 15 s and sonicated for 30 s in an ultrasonic bath to dissolve/ disrupt the particles, agitated again for more 15 s, transferred to a 1.5-L Eppendorf vial, centrifuged in an ultracentrifuge at 14,000 rpm for 10 min and then filtered through a 0.22-mm PTFE membrane to an auto sampler vial.

Method optimisation The extraction procedure was defined by single paired instrumental responses and recovery comparison of kidney samples spiked with analytes at 1.0 MRLs (2.00 0.10 g). The following changes on the analytical steps were tested, one at a time: (a) changing the extraction buffer, (b) changing the washing cartridge step, (c) addition of the hexane cartridge washing step after elution and (d) change on the ammonia concentration of the acetonitrile/ammonia solution from 99.5:0.5 to 98:2 to 96:4.

Method validation according to 2002/657/EC The validation procedure for bovine kidney samples was performed according to the 2002/657/EC procedures, and linearity range, matrix effect, accuracy, precision, decision limit (CC ), detection capability (CC ) and selectivity were considered. The validation was expanded to other species such as swine, equine and poultry.

Selectivity The validation procedure began with the analysis of six blank kidney samples to check possible interferences and equivalent analyte responses; one blank sample was fortified at the lowest point of the curve (0.5 MRL). The other samples were injected and the results of blank samples were assessed for the equivalence of the analyte response in blank samples on the sample fortified at the lowest calibration point. To demonstrate whether another group of antibiotics potentially present in the samples could affect the

results by masking or increasing them, 18 blank samples fortified at 0.5, 1.0 and 1.5 MRL have also been analysed. Two samples of each level received 200 mL of an aminoglycoside standard solution containing spectinomycin, streptomycin, dihydrostreptoamikacin, apramycin, mycin (0.4 mg mL 1), tobramycin, gentamicin, neomycin, hygromycin (1.0 mg mL 1) and kanamycin (2.0 mg mL 1) in order to evaluate the methodâ&#x20AC;&#x2122;s selectivity. This experiment was repeated in two further occasions.

Linearity of the response The linearity study was carried out by preparing standard solution, matrix-matched and spiked calibration curves using six calibration points at concentrations varying from zero, 0.50, 0.75, 1.00, 1.25 and 1.50 MRL, in triplicate injections, on 3 different days.

Matrix effect The tissue-matrix effect studies were performed by the extraction of five matrix-matched samples fortified at concentrations ranging from zero to 1.50 MRL. A standard solution calibration curve at the same concentration levels was also prepared. Samples were injected in triplicate. The experiment was repeated in two further occasions. The interspecies matrix effect was evaluated by a paired point-to-point comparison of bovine calibration curves to swine, equine and poultry calibration curves obtained by the extraction of kidney samples of 2.00 0.10 g, spiked with mixed work standard solutions at concentrations varying from zero to 1.50 MRL.

Precision Repeatability was assessed by performing the tests on 18 blank samples spiked at 0.50, 1.00 and 1.50 concentration levels. This experiment was repeated in two further occasions. Reproducibility was evaluated from the experimental run repeatability in different conditions and by different analysts.

Decision limit and detection capability The decision limits (CC ) and detection capabilities (CC ) were calculated according to ISO 11843, as described in the Commission Decision 2002/657/EC. The calibration curves were obtained by analysis and extraction of five blank samples fortified at concentration levels of 0.50, 0.75, 1.00, 1.25 and 1.50 MRL. A blank sample was also analysed with no added standard. Samples were injected in triplicate. The experiment was repeated on two further occasions.

Food Additives and Contaminants Table 2. Retention times for LIN, CLI, ERI, TIM and TYL. Retention time/standard deviation (minutes) Analyte

Solvent standards

Matrix matched samples

Spiked samples

Lincomycin Tilmicosin Clindamycin Erythromycin Tylosin

4.34 0.02 7.62 0.03 7.35 0.02 8.83 0.02 9.38 0.02

4.28 0.01 7.58 0.01 7.32 0.01 8.79 0.01 9.35 0.01

4.31 0.01 7.60 0.01 7.34 0.01 8.82 0.01 8.77 0.01

Results and discussion Method optimisation The LC-MS/MS method was developed for confirmatory analysis of kidney tissue for lincomycin (LIN), clindamycin (CLI), tilmicosin (TIM), erythromycin (ERI) and tylosin (TYL) residues. The MS/MS fragmentation conditions and collision energies were investigated by direct infusion of individual standard solvent solutions. For confirmation, the precursor ions [M þ 1]þ and two transition products of each compound were monitored (Table 1), resulting in four identification points for LIN, CLI, ERI and TYL (1 precursor þ 2 transition products) and 5.5 points for TIM (1 precursor þ 3 transition products). Because no deuterated or C–13-labelled standards were commercially available at the time of this study, matrix effects on ionisation in the source of the mass spectrometer were minimised by adjusting the mobile phase composition such that the analytes eluted well after the void-volume of the column. It was observed that the matrix or the extraction procedure did not significantly affect the intensity of the diagnostic ions from the pure standard, showing the absence of matrix suppression of these ions. The analytes were chromatographed on a Phenomenex Luna C18(2) column, and the retention times were determined for each analyte in spiked samples. Matrix-fortified samples were not significantly different from those determined using the solvent analytes (Table 2). It was observed that the recovery of the analytes depended on the extraction buffer pH. For this evaluation, mixed work standard solutions with no tissue samples were extracted using extraction buffer solutions of pH 4.5, 6.0, 7.5, 8.0 and 10.0. The comparison of instrumental responses obtained after extraction with these solutions demonstrated that the best results were obtained when a pH 8.0 extraction buffer solution was used (Figure 2). In addition, modifications to the extraction steps were made to improve analyte recoveries. The methanol/water washing cartridge step was removed due to decreased analyte recovery variation of the concentration of

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ammonia 99.5:0.5 to 98:2. A 2 mL hexane washing step before the extract elution was also included to remove fat impurities and, thus, extend the life of the chromatographic column and reduce the formation of solid impurities on the mass detector plate.

Selectivity Negative control samples of bovine kidney were analysed and proved to be free of the antibiotics of interest and interference of endogenous or exogenous components. The results of the interference studies indicated that none of the aminoglycoside antibiotics affected the results by masking, adding or subtracting analytes from samples. Figure 3 displays the comparison of tilmicosin LC-MS/MS chromatograms of blank samples spiked at the 1.0 MRL level without aminoglycosides addition (left) and blank samples spiked at 1.0 MRL analytes with aminoglycosides addition (right). There was no observable aminoglycoside interference for all transitions. In addition, the instrumental responses obtained from the spiked samples at three analyte levels in the presence and absence of aminoglycosides were compared using the T-test (95% confidence level). The test results (Table 3) showed that the calculated values are below the critical values of T and, consequently, no effect of the addition of aminoglycosides was observed on the detector response intensities.

Matrix effects and interspecies matrix effects The matrix effect was evaluated by point-to-point mean comparisons of the analyte responses in the matrixmatched fortified samples and standard solutions, in the same range of concentrations as the calibration curves. Matrix-matched and standard solution calibration curves from three different days were analysed and compared. A comparison of the means in the same level for both matrix-matched and standard calibration curves was made, first using the F variance homogeneity statistical test to evaluate the variance equivalence, and accordingly to the equivalence result, the T-test for equivalent or nonequivalent variances was used. Both curves were linear over the calibration range of 0.50–1.50 MRLs for all analytes and in all analytical occasions (days). It was observed that the matrix effect on the responses of the analytes was influenced by the concentration levels and analytical occasions and thus significant. A possible explanation is the absence of internal standards to adjust the instrumental response variation and, thus, minimise the concentration and analytical occasion’s effects. Considering this, the solution adopted was the use of spiked calibration curves to minimise the matrix effect on the response leading to more reliable results.

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Figure 2. Comparison of the instrumental response of clindamycin, erythromycin, lincomycin, tylosin and tilmicosin extracted with buffers pH 4.5, 6.0, 7.5, 8.0 and 10.0.

Figure 3. Comparison of tilmicosin LC-MS/MS chromatograms of blank samples spiked at the 1.0 MRL level without aminoglycosides addiction (left) and blank samples spiked at 1.0 MRL analytes with aminoglycosides addition (right).

The same approach used for the matrix effect evaluation was also adopted for the method extension to swine, poultry and equine matrices but, in this case, the ‘‘interspecies matrix effect’’ was evaluated by the paired comparison of the bovine spiked calibration

curve with other species’ spiked curves. The obtained statistical results demonstrate that the matrix effect on the responses is influenced by species, although the same kind of tissue had been used. It was concluded that for the quantitative analyses of these compounds,

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Table 3. T-test results for comparison of detector response intensities values for lincomycin and macrolides in the presence and absence of aminoglycosides (95% confidence level). Instrumental response (in CPS) Analyte Lincomycin

Concentration (mg/kg)

Without AMGs

With AMGs

Tcalc

Tcrit (95%)

Test result

750

431,366 464,442 536,454 924,088 1,151,787 1,525,096 37,913 39,155 68,309 59,059 82,169 98,216 70,983 68,604 132,208 132,978 172,537 214,960 80,615 83,482 140,809 144,295 207,837 235,734 17,333 17,635 27,575 28,265 41,289 47,161

415,043 493,988 1,021,802 744,426 1,244,591 1,145,353 35,828 42,564 64,010 65,238 101,894 95,571 69,087 74,093 138,362 133,681 200,538 204,819 73,335 74,244 135,529 146,821 233,605 247,936 14,662 15,059 26,329 27,156 43,952 45,896

0.04

2.57

Tcalc < Tcrit

0.93

2.57

Tcalc < Tcrit

0.90

2.57

Tcalc < Tcrit

0.56

2.57

Tcalc < Tcrit

1.31

2.57

Tcalc < Tcrit

1500 2250 Tilmicosin

500 1000 1500

Clindamycin

100 200 300

Erythromycin

100 200 300

Tylosin

50 100 150

Note: Tcalc ¼ calculated T value and Tcrit ¼ critical T value.

the most suitable calibration function was the spiked calibration curve for individual species. Linearity Quantification requires that the dependency between the measured response and the analyte concentration is known. The estimation of this functional relationship, called the calibration equation, was performed using the statistical least squares method. The calibration curve was obtained by internal or external standardisation and formulated as a mathematical expression used to calculate the analyte concentration to be determined in real samples (Meier and Zu¨nd 2000). The linearity of the chromatographic response was tested with spiked calibration curves using six calibration points in concentrations ranging from 0 to 1.50 MRL. The homogeneous (homoscedastic) or heterogeneous (heteroscedastic) scatter across the concentration range of the curves were considered in the curve-fitting as well as in the regression coefficients (r2). The calibration data parameters obtained for each antibiotic are shown in Table 4.

The linear range for all analytes varied from 0 to 1.50 MRL values and the regression coefficients (r2) for all the used calibration curves were 0.89. It was also observed that there were variations in the standard deviation at different concentrations levels (heteroscedasticity), leading to the conclusion of a weighted linear regression model for the obtained spiked calibration curves.

Decision limits and detection capability The decision limit (CC ) and the detection capability (CC ) were calculated following the ISO 11843 Directive. The values are indicated in Table 5. CC and CC values for swine, equine and poultry species were also obtained. Error probability ( ) and the statistical certainty (1 ) were assumed as 5%.

Precision and accuracy The values obtained for the method precision and accuracy are indicated in Table 6. The precision of the

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Table 4. Calibration data parameters for lincomycin, clindamycin, tilmicosin, erythromycin and tylosin.

Analyte

Concentration range (mg kg 1)

Lincomycin

0 to 2250

Tilmicosin

0 to 1500

Clindamycin

0 to 300

Erythromycin

0 to 300

Tylosin

0 to 150

Occasion

Calibration equations

Homogeneity of variance

Regression model adopted

1st day 2nd day 3rd day Grouped 1st day 2nd day 3rd day Grouped 1st day 2nd day 3rd day Grouped 1st day 2nd day 3rd day Grouped 1st day 2nd day 3rd day Grouped

Y ¼ 7.333 þ 482.033 Y ¼ 60.321 þ 996.475 Y ¼ 89.415 þ 849.915 Y ¼ 51.556 þ 736.704 Y ¼ 262.392 þ 68.950 Y ¼ 48.472 þ 109.485 Y ¼ 322.680 þ 114.990 Y ¼ 163.111 þ 92.868 Y ¼ 503.349 þ 682.079 Y ¼ 21.666 þ 1374.094 Y ¼ 41.935 þ 1379.926 Y ¼ 21.556 þ 1046.003 Y ¼ 43.753 þ 722.465 Y ¼ 21.328 þ 1257.543 Y ¼ 18.646 þ 1088.351 Y ¼ 27.778 þ 931.091 Y ¼ 27.873 þ 284.496 Y ¼ 2726.619 þ 495.989 Y ¼ 33.957 þ 434.663 Y ¼ 20.222 þ 358.138

X X X X X X X X X X X X X X X X X X X X

0.998 0.982 0.865 0.905 0.988 0.966 0.987 0.933 0.998 0.982 0.987 0.894 0.996 0.983 0.989 0.952 0.994 0.947 0.988 0.932

Heteroscedastic

Weighted

Heteroscedastic

Weighted

Heteroscedastic

Weighted

Heteroscedastic Homoscedastic Heteroscedastic Heteroscedastic Heteroscedastic

Weighted Unweighted Weighted Weighted Weighted

Table 5. Lincomycin and macrolides values of the decision limits (CC ) and detection capabilities (CC ). Bovine Analyte Lincomycin Tilmicosin Clindamycin Erythromycin Tylosin

Poultry

Swine

Equine

CC (mg kg 1)

1596.4 1034.6 212.9 211.4 106.8

1692.8 1069.2 225.8 222.8 113.6

1596.4 1071.8 201.3 209.9 102.8

1692.8 1143.7 220.6 219.7 105.6

1573.0 1053.9 220.3 201.7 109.0

1646.0 1107.8 240.6 221.4 118.1

1698.3 1089.4 221.7 215.4 103.6

1896.7 1178.8 243.4 230.1 107.2

Table 6. Intra- and inter-assay variation for the accuracy for lincomycin and macrolides antibiotics in bovine kidney.

Analyte Lincomycin Tilmicosin Clindamycin Erythromycin Tylosin

Repeatability %RSD

Within-laboratory reproducibility %RSD

Recovery %

Uncertainty (mg kg 1)

21.6 18.6 15.8 12.1 16.6

21.4 17.2 12.9 10.9 15.5

102.0 92.5 98.4 97.6 100.6

260.4 169.9 24.5 26.6 16.2

Note: The expanded uncertainty values refers to 1.0 MRL level

method, expressed as the relative standard deviation (RSD%) in terms of repeatability at three levels of fortification (n ¼ 54), for tilmicosin, clindamycin, erythromycin and tylosin was less than 18.6%. The RSD% values were less than 21.6%

for lincomycin. In terms of reproducibility (n ¼ 108), the RSD% values for tilmicosin, clindamycin, erythromycin and tylosin were less than 17.2%. The RSD% values were less than 21.4% for lincomycin. The higher RSD% values for lincomycin indicate the need to

Food Additives and Contaminants incorporate structurally identical isotopically labeled lincomycin as an internal standard in the method, although it was suitable for quantitative analysis considering the RSD reproducibility values. The repeatability RSD values are acceptable according to the European Commission guidelines, except for lincomycin. For reproducibility, all the RSDs are also acceptable according to the European Commission guidelines. Accuracy was estimated using spiked calibration curves and calculated by comparing the analyte experimental concentration to the added concentration and expressed as percentage recovery. The recoveries obtained from six replicates, within-day assays at three different concentration levels, were in the range of 92.5–102% for all analytes.

Measurement uncertainty The within-laboratory reproducibility and the calibration curves were considered to estimate the combined measurement uncertainty. The calibration curve uncertainty and the intermediate precision were calculated and used to obtain the combined measurement uncertainty and multiplied by a factor of 2 (k) to obtain the expanded uncertainty. The expanded uncertainties for the analytes are shown in Table 6.

Conclusion The detection and quantification by LC-MS/MS proved to be a sensitive technique for analysing the antibiotics lincomycin, clindamycin, tilmicosin, erythromycin and tylosin in kidney samples of cattle, swine, equine and poultry at residue levels. The proposed method separates the four macrolides and lincosamicyn with a reasonable resolution, in an 18-min single chromatographic run. The solid phase extraction (SPE) cartridge with mixed adsorbent C8 and cation exchange resin was used to pre-concentrate the analytes and to eliminate kidney interferences in a simple and rapid procedure, providing a relatively simple extraction. Moreover, only 2 g of tissue were needed per sample. The validation steps performed were in accordance with Decision 2002/657/EC, and the results of the validation process showed that this method was suitable for application for the Brazilian Residues and Contaminants Control Plan (PNCR).

References Adams SJ, Fussell RJ, Dickinson M, Wilkins S, Sharman M. 2009. Study of the depletion of lincomycin residues in

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honey extracted from treated honeybee (Apis mellifera L.) colonies and the effect of the shook swarm procedure. Analytica Chimica Acta. 637:315–320. Berrada H, Borrull F, Font G, Molto´ JC, Marce´ RM. 2007. Validation of a confirmatory method for the determination of macrolides in liver and kidney animal tissues in accordance with the European Union regulation 2002/657/ EC. J Chromatogr A. 1157:281–288. Codex Alimentarius Commission. 2003. Report of the twenty-sixth Codex Committee on Residues of Veterinary Drugs in Food, ALINORM 03/23. Codony R, Compan˜o´ R, Granados M, Garcı´ a-Regueiro JA, Prat MD. 2002. Residue analysis of macrolides in poultry muscle by liquid chromatography-electrospray mass spectrometry. J Chromatogr A. 959:131–141. Council Regulation (ECC) No. 2377/90 of 26 June 1990. Laying down a community procedure for the establishment of maximum residue limits of veterinary medicinal products in foodstuffs of animal origin. EC OJ. L224, 18.08.1990. Draisci R, Palleschi L, Ferretti E, Achene L, Cecilia A. 2001. Confirmatory method for macrolide residues in bovine tissues by micro-liquid chromatography-tandem mass spectrometry. J Chromatogr A. 926:97–104. European Commission. 2002. Commission Decision 2002/ 657/EC of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Brussels: European Commission. European Medicines Agency. 2008. [cited 2010 February 18]. Available from: http://www.emea.europa.eu Granelli K, Elgerud C, Lundstro¨m A, Ohlsson A, Sjo¨berg P. 2009. Rapid multi-residue analysis of antibiotics in muscle by liquid chromatography-tandem mass spectrometry. Analytica Chimica Acta. 637:87–91. International Organization for Standardization. ISO 11843:1997. Capability of detection – Part 1: terms and definitions, Part 2: methodology in the linear calibration case. Geneva, Switzerland: ISO 2000. International Organization for Standardization. ISO/IEC 17025:2005. General requirements for the competence of testing and calibration laboratories. Geneva, ISO. McGlinchey TA, Rafter PA, Regan F, McMahon GP. 2008. A review of analytical methods for the determination of aminoglycoside and macrolide residues in foods matrices. Analytica Chimica Acta. 624:1–15. Meier PC, Zu¨nd RE. 2000. Statistical methods in analytical chemistry. 2nd ed. Hoboken (NJ): John Wiley & Sons. p. 424. Peters RJB, Bolck YJC, Rutgers P, Stolker AAM, Nielen MWF. 2009. Multi-residue screening of veterinary drugs in egg, fish and meat using high-resolution liquid chromatography accurate mass time-of-flight mass spectrometry. J Chromatogr A. 1216:8206–8216. Stubbings G, Bigwood T. 2009. The development and validation of a multiclass liquid chromatography tandem mass spectrometry (LC-MS/MS) procedure for the determination of veterinary drug residues in animal tissue using a QuEChERS (QUick, Easy, CHeap, Effective, Rugged and Safe) approach. Analytica Chimica Acta. 637:68–78.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 596–601

Validation of a rapid and sensitive routine method for determination of chloramphenicol in honey by LC–MS/MS Tsuyoshi Taka*, Marina C. Baras and Zahra F. Chaudhry Bet Ministe´rio da Agricultura, Pecua´ria e Abastecimento (MAPA), Laborato´rio Nacional Agropecua´rio (Lanagro), Rua Raul Ferrari, S/N, Jardim Santa Marcelina, 13100-105 Campinas, Sa˜o Paulo, Brazil (Received 26 November 2010; final version received 31 August 2011) Chloramphenicol (CAP) is a broad spectrum antibiotic used in the treatment of human and animal diseases. However, CAP can exhibit toxic effects in certain susceptible individuals, causing bone marrow depression, including fatal aplastic anemia. As this condition is dose-independent, CAP has been banned for use in food-producing animals, including honeybees. In this study, a quick, simple and low-cost routine analytical method was developed for the screening and confirmation of chloramphenicol in honey by LC–MS/MS. Sample clean-up takes only two steps without SPE procedure and with recoveries >97%. Honey samples were selected from several producers in Brazil and diluted in a small amount of water. After fortification and addition of ds-chloramphenicol as internal standard, the samples were extracted with ethyl acetate. Complete validation of the method was performed on the basis of EU decision 2002/657. Within-laboratory CV reproducibility at the lowest concentration was <10%. An evaluation of two different methods to calculate the decision limit and detection capability gave 0.08 mg kg 1 for CC and 0.12 mg kg 1 for CC . Keywords: honey; veterinary drug residues; chloramphenicol; chromatography; LC/MS

Introduction Chloramphenicol (CAP) is an inexpensive, potent, broad-spectrum antibiotic, exhibiting activity against both gram-positive and gram-negative bacteria as well as other groups of micro-organisms. In human medicine, however, its use is limited because it is often associated with serious side-effects, such as the development of aplastic anemia. Since this condition is doseindependent, the use of CAP in livestock, including honeybees, has been banned in the European Union, the United States of America, Canada and many other countries. As a consequence, CAP is included in Annex IV of Council Decision 2077/90, which comprises drugs with an established zero-tolerance level in edible tissues. However, in 2001 and 2002, CAP residues were detected in various foodstuffs imported into the EU from Asian countries. This had a major impact on international trade, and restrictions were placed on the importation of these products. To monitor and control the compliance of a zero tolerance level of CAP, sensitive, accurate and robust analytical methods are needed. Since then, many methods have been developed to achieve this goal, and publications cover a variety of extraction techniques, such as solid phase extraction (SPE), molecular imprinted polymers, *Corresponding author. Email: tsuyoshi.taka@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Brazilian Ministry of Agriculture http://dx.doi.org/10.1080/19440049.2011.625047 http://www.tandfonline.com

polymer monolith extraction and biosensors (Verzegnassi et al. 2003; Ashwin et al. 2005; Asp et al. 2006; Huang et al. 2006; Schirmer and Meisel 2006; Sheridan et al. 2008; Rejtharova´ and Rejthar 2009). In this paper, a simple and sensitive method for confirmatory detection of CAP is presented. Its validation is in accordance to EU decision 2002/657.

Materials and methods Instrumentation The LC–MS/MS system consisted of quaternary pump liquid chromatographic system and autosampler, part of the Agilent Series 2000, coupled to an API 5000 triple quadrupole mass spectrometer. The Cl8 Luna column (50 2 mm, 5 mm and 100 A˚) was purchased from Phenomenex.

Reagents and standards All reagents were of analytical grade quality and solvents such as methanol and ethyl acetate were of at least HPLC-grade. Ultrapure water was obtained via a Purelab system from Elka. CAP standard and d5-CAP (internal standard) was purchased from Sigma– Aldrich. Primary stock standard solutions of CAP

Food Additives and Contaminants Table 1. Mobile phase gradient: 2 mM ammonium acetate (Phase A) and methanol (Phase B).

Table 2. Parameters of MS/MS detector. Ionisation

Time (min) Pre-run (6 min) 0 5 9

Phase A

Phase B

Flow-rate (ml min 1)

80 80 10 10

20 20 90 90

300 300 300 300

and d5-CAP were prepared in acetonitrile at the concentration of 200 mg ml 1. Suitable working solutions used for spiking blank samples were obtained by dilution in methanol to give a concentration of 10 ng ml 1 for CAP and 20 ng ml 1 for d5-CAP. All solutions were stored at 10 C

Extraction procedure Initially, 1 g of honey was dissolved in water (l ml) and kept at 40 C for 1 h. This mixture was then vortexed until a homogeneous sample was obtained (Martins Ju´nior et al. 2006). The samples were fortified with 1.0 mg kg 1 internal standard (d5-CAP) and with CAP at concentrations of 0.2, 0.25, 0.4, 0.6 and l.0 mg kg 1 for analytical curves. As 0.3 mg kg 1 is the MRPL of this method, samples spiked at 0.3, 0.45 and 0.6 mg kg 1 were used during validation studies. To extract CAP, ethyl acetate was added (4 ml) and the sample was mixed vigorously for l min. Following centrifugation at 2450 g for 5 min, 2 ml of the organic layer was transferred to a test tube and evaporated to dryness under nitrogen at 45 C. The residue was reconstituted in 2 ml of a methanol/water (50:50, v/v) solution and sent for LC–MS/MS analysis. LC–MS/MS analysis Chromatographic separation was performed on a Luna (Phenomenex) ODS C18 5-mm column (50 2 mm I.D., 100 A˚) by using a mobile phase gradient of 2 mM ammonium acetate (Phase A) and methanol (Phase B), as presented in Table 1. The flowrate was set at 0.3 ml min 1, the injection volume at 20 ml and the column temperature at 24 C. The MS detector was operated according to the parameters listed in Table 2. Results and discussion A MS scan of CAP shows a typical isotopic pattern due to the two Cl atoms in the molecule (Asp et al. 2006). The most abundant molecular ion of chloramphenicol is m/z 321 and the fragmentation of this parent ion, as can be seen in Figure 1, resulted in 4 products ions: m/z 152, 176, 194 and 257. All these transitions were investigated. The most intense ion m/z

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Scan type Monitored ions (one precursor and two fragments) Source temp. Nebuliser Turbo ion Curtain gas Collision gas Ion spray voltage Entrance potential Dwell time

Negative ion mode with a TurboIon SprayTM MRM CAP: m/z 321!152; 321!194 and 321!257 d5-CAP (I.S.): m/z 326!157 650 C 40 40 12 6 3000 V 10 250.0 ms

152 is the obvious choice as quantitative ion, while m/z 257 and m/z 194 were investigated to find the best confirmatory ion (Vinci et al. 2005). Deuterated CAP (d5-CAP) was introduced as internal standard and, for the same reasons, the monitored ion was m/z 326!157. Method validation was carried out according to criteria set by Commission Decision 2002/657/EC. Parameters taken into account were: specificity, linearity, repeatability, reproducibility, recovery, accuracy, decision limit (CC ), detection capability (CC ), robustness plus LOD and LOQ. These two parameters were determined to satisfy validation requirements prescribed by Brazilian Regulations (Instruc¸a˜o normativa no. 42, PNCR, 20/12/1999) from the Brazilian Ministry of Agriculture (Mauricio et al. 2009). Specificity and linearity A specificity study was conducted to verify the absence of potential interfering compounds at the retention time of CAP and consisted of the analysis of blank matrix samples. In the present study, 22 honey samples originating from four different regions of Brazil were employed. Besides being geographically varied, the samples used were also visually different, ranging from light to dark honey and from different flower sources, mostly from wild flowers, but also from orange blossom and eucalyptus. No interferences at the same retention time as CAP were found in the matrix samples. Figure 2 shows chromatograms of a blank (a) and spiked sample of honey (b). Linearity assays were performed by plotting calibration curves of CAP at concentrations of 0.10, 0.25, 0.40, 0.6 and 1.00 mg kg 1 and containing a fixed amount of d5-CAP (1.0 mg kg 1) in seven replicates. Linearity was evaluated from the graph through the regression coefficient (r2), with all curves showing an r2 > 0.995. Repeatability, reproducibility, recovery and accuracy For repeatability, the agreement between the results obtained under the same conditions (same analyst,

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Figure 1. Typical fragmentation of chloramphenicol parent ion, m/z 321, showing 4 products ions: m/z 152, 176, 194 and 257.

Table 3. Parameters evaluated in validation study for CAP. Parameter Repeatability Reproducibility Recovery Accuracy LOD LOQ CC CC CC **

Unit Mean CV Mean SD CV

mg kg 1 % mg kg 1 % % % mg kg 1 mg kg 1 mg kg 1 mg kg 1 mg kg 1

1.0 MRPL 0.31 4.07 0.31 0.020 6.4 101.9 104.0 0.04 0.11 0.08 0.12 0.10

1.5 MRPL 0.45 2.39 0.44 0.013 2.9 99.0 100.0

2.0 MRPL 0.58 3.48 0.59 0.022 3.9 97.0 99.9

Notes: *Graphically calculated. **From 20 samples spiked at CC level.

equipment, reagents, etc.) over three different days was evaluated via the mean and CV values. In the same way, reproducibility was evaluated via CV and the SD of three different batches performed over 3 days by two analysts. The validation parameters of this method for CAP determination were summarized in Table 3. Recovery was calculated with spiked negative samples, as in the repeatability study, at three different levels: 0.3, 0.45 and 0.6 mg kg 1. All recovery results were in the range 97.0â&#x20AC;&#x201C;101.9%. According to EU Decision 2002/657, these values fall within acceptable ranges.

Accuracy was assessed through recovery results and, as expected, was in the range 70â&#x20AC;&#x201C;110%. The present method gave very good results, ranging between 99.9 and 103.9%.

LOQ, LOD, CC and CCb The signal of a CAP-spiked matrix three times higher than the blank matrix plus the standard deviation (SD) versus t (for n degrees of freedom) was accepted as the

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Figure 2. LCâ&#x20AC;&#x201C;MS/MS chromatograms of honey samples, MRM mode: blank matrix (a), 0.30 mg kg 1 spiked matrix (b).

limit of detection (LOD). In the same way, for limit of quantification (LOQ), the concentration was calculated as a 10 times the SD of blank samples plus the mean signal of a blank matrix. The decision limit (CC ) was calculated using fortified blank material. The signal was plotted against the added concentration and the corresponding concentration at the y-intercept plus 2.33 times the SD of the within-laboratory

reproducibility. Detection capability (CC ) was calculated in two different ways. The first consisted of taking the concentration at the decision limit plus l 0.64 times the SD of the within-laboratory reproducibility of the mean measured content at the lowest concentration of the spiked blank. CC was also determined via analysis of 20 blank samples spiked at CC (0.08 mg kg 1). The results for both methods were

600

T. Taka et al. Table 4. Results for robustness test. Parameter Sample dilution Heating time Heating temperature Evaporation temperature Phase A ammonium acetate concentration Phase B methanol percentage Ethyl acetate brand

Original method value

Tested value

Calculated F factor

1 ml water 60 min 40 C 45 C 2.0 mM 95% Mallincrodt

1.5 ml water 30 min 45 C 40 C 1.0 mM 100% Burdick & Jackson

2.81 1.37 2.75 1.12 1.82 1.03 2.45

similar, proving that, in this method, CAP can be quantified using d5-CAP as internal standard at mg kg 1 in levels below the MRPL of 0.3 mg kg 1. All these results are shown in Table 3.

Robustness Robustness was estimated via the Youden robustness test. This experimental design involves eight experiments and the selection of seven variables, chosen during sample preparation and analysis. The application of this test consists of the introduction of minor simultaneous changes in these parameters according to an established experimental design, with the aim of identifying the critical factors that may have to be controlled to obtain accurate assay results. The effect of each factor was estimated by determining the difference between the mean result for the variable at a ‘‘high level’’ (designated by a capital letter) and at a ‘‘low level’’ (indicated by a small letter). The factors taken into account in this study and their levels of variation were: (a) sample dilution, (b) heating time, (c) heating temperature, (d) evaporating to dryness temperature, (e) concentration of ammonium acetate in mobile phase A, (f) percentage of methanol in mobile phase B, and (g) ethyl acetate brand. For this study, eight batches of 0.3 mg kg 1 fortified samples were tested. The statistical F factor was calculated for all the results from these assays and none of them proved to be a critical point for analysis. Results obtained are presented in Table 4. In addition to this overall result, the standard deviation of the differences Di (SDi) was calculated according to the Youden approach: ffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X Di2 SDi ¼ 2 7 As the obtained value (1.4%) was less than the standard deviation of the within-laboratory reproducibility at 0.3 mg kg 1 (2.0%), it was demonstrated that all selected factors together do not significantly affect the analytical performance.

All calculated F factors are lower than 9.28, which includes the tabulated critical F factor for three degrees of freedom. This proves that subtle variations in the evaluated parameters have no significant impact on the results of the analysis, and that the method is robust even at trace levels.

Conclusions A quantitative method has been developed for the determination of trace levels of chloramphenicol in honey from different origins. Tandem mass spectrometry using MRM transitions of specific fragment ions permitted a very selective, sensitive and robust method for confirmatory detection. This method proved to be fairly robust and capable of withstanding minor fluctuations in operating variables that may occur during routine application. The main properties of the method are the simplicity of the extraction and clean-up, thus representing an improvement on previously published methods. The method has been validated according to EU decision 2002/657 for the analysis of veterinary drug residues and has been successfully applied in the Brazilian Government’s residue control programme for honey (Mauricio et al. 2009).

Acknowledgements The authors thank Aparecida Rocha Chagas for technical assistance, Applied Biosystems for the technical support and Brazilian Ministry of Agriculture for financial support.

References Ashwin HM, Stead SL, Taylor JC, Startin JR, Richmond SF, Homer V, Bigwood T, Sharman M. 2005. Development and validation of screening and confirmatory methods for the detection of chloramphenicol and chloramphenicol glucuronide using SPR biosensor and liquid chromatography–tandem mass spectrometry. Anal Chim Acta. 529:103–108.

Food Additives and Contaminants Asp TN, Ronning HT, Einarsen K. 2006. Determination of chloramphenicol residues in meat, seafood, egg, honey, milk, plasma and urine with liquid chromatography– tandem mass spectrometry, and the validation of the method based on 2002/657/EC. J Chromatogr A. 1118:226–233. Huang JF, Zhang HJ, Feng YQ. 2006. Chloramphenicol extraction from honey, milk, and eggs using polymer monolith microextraction followed by liquid chromatography–mass spectrometry determination. J Agric Food Chem. 54:9279–9286. Martins Juńior HA, Bustillos OV, Pires MAF, Lebre DT, e Wang AY. 2006. Determinaçaõ de resı´ duos de cloranfenicol em amostras de leite e mel industrializados utilizando a tećnica de espectrometria de massas em ‘‘Tandem’’ (CLAE-EM/EM). Quim Nova. 29: 586–592. Mauricio AQ, Lins ES, Alvarenga MB. 2009. A national residue control plan from the analytical perspective – The Brazilian case. Anal Chim Acta. 637:333–336. Ministe´rio da Agricultura e do Abastecimento. 1999. Instruçaõ Normativa no 42, de 20/12/99. Brası´ lia: Ministe´rio da. Agricultura e do Abastecimento.

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Rejtharova´ M, Rejthar L. 2009. Determination of chloramphenicol in urine, feed water, milk and honey samples using molecular imprinted polymer clean-up. J Chromatogr A. 1216:8246–8253. Schirmer C, Meisel H. 2006. Synthesis of a molecularly imprinted polymer for the selective solid-phase extraction of chloramphenicol from honey. J Chromatogr A. 1132:325–328. Sheridan R, Policastro B, Thomas S, Rice D. 2008. Analysis and occurrence of 14 sulfonamide antibacterials and chloramphenicol in honey by solid-phase extraction followed by LC/MS/MS analysis. J Agric Food Chem. 56:3509–3516. Verzegnassi L, Royer D, Mottier P, Stadler RH. 2003. Analysis of chloramphenicol in honeys of different geographical origin by liquid chromatography coupled to electrospray ionization tandem mass spectrometry. Food Addit Contam. 20:335–342. Vinci F, Guadagnuolo G, Danese V, Salini M, Serpe L, Gallo P. 2005. In-house validation of a liquid chromatography/electrospray tandem mass spectrometry method for confirmation of chloramphenicol residues in muscle according to Decision 2002/657/EC. Rapid Commun Mass Spectrom. 19:3349–3355.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 602–608

Validation of an LC-MS/MS method for malachite green (MG), leucomalachite green (LMG), crystal violet (CV) and leucocrystal violet (LCV) residues in fish and shrimp Jociani Ascaria, Se´rgio Draczb*, Fla´vio A. Santosb, J.A. Limab, Maria Helena G. Dinizb and Eugeˆnia A. Vargasb a Bolsista CNPq, Laborato´rio Nacional Agropecua´rio (LANAGRO), Pedro Leopoldo, Brazil; bLaborato´rio de Resı´duos de Medicamento Veterina´rios, Laborato´rio Nacional Agropecua´rio (LANAGRO), Pedro Leopoldo, Brazil

(Received 22 November 2010; final version received 16 December 2011) A quantitative liquid chromatography–tandem mass spectrometry (LC-MS/MS) method for the simultaneous analyses of malachite green (MG), crystal violet (CV) and its major metabolites, leucomalachite green (LMG) and leucocrystal violet (LCV) residues in fish and shrimp samples has been validated. Fish and shrimp samples were extracted with citrate buffer/acetonitrile, and the extracts were purified on strong cation-exchange (SCX) solid-phase extraction (SPE) cartridge. After conversion of LMG into MG using a post column oxidation reactor containing lead (IV) oxide (PbO2), the effluents were analysed. Residues were analysed using positive-ion electrospray ionisation (ESI). Identification and quantification of analytes were based on the ion transitions monitored by multiple reaction monitoring (MRM). Validation of the method was carried out in accordance with the Decision 2002/657/EC, which establishes criteria and procedures for the validation of methods. The following parameters were determined: decision limit (CC ), detection capability (CC ), linearity, accuracy, precision, selectivity, specificity and matrix effect. The decision limits (CC ) for MG, LMG, CV and LCV were 0.164, 0.161, 0.248 and 0.860 mg kg–1. The respective detection capabilities (CC ) were 0.222, 0.218, 0.355 and 1.162 mg kg–1. Typical recoveries (intermediate precision) in shrimp, for MG, CV, LMG and LCV for 2.0 mg kg–1 level fortified samples using the optimised procedure were in the range 69%, 97%, 80.3% and 71.8%, respectively. The findings demonstrate the suitability of the method to detect simultaneously MG, CV and its metabolite (LMG and LCV) in fish and shrimp. Keywords: shrimp; fish; malachite green; leucomalachite green; dyes; analysis; method validation

Introduction Malachite green (MG) dye, although forbidden, has been widely used in the fish industry as an antimicrobial, antiseptic and ectoparasitic agent. Crystal violet (CV) is also effective in the treatment of fungal infections and is usually used together with MG (Dowling et al. 2007; Arroyo et al. 2008). Studies show that both MG as CV and its metabolites are potentially mutagenic and carcinogenic (Mittelstaedt et al. 2004; Stammati et al. 2005; Turnipseed et al. 2005); therefore, their use is not allowed as veterinary medicine in fish and shrimp breeding (Arroyo et al. 2008; Yuan et al. 2009). When MG and CV are absorbed by fish, most of the substances are rapidly reduced to the no-chromophore metabolites leucomalachite green (LMG) and leucocrystal violet (LCV), which are persistent forms in the tissue (Rushing and Thompson 1997). Some methods for the analysis of MG and LMG (Andersen et al. 2005; Scherpenisse and Bergwerff 2005; Turnipseed et al. 2005; Halme et al. 2007; Hall et al. 2008; Martı´ nez et al. 2010) also simultaneously *Corresponding author. Email: sergio.dracz@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.653695 http://www.tandfonline.com

determine CV and its metabolite (Dowling et al. 2007; Chen and Miao 2010; Wang et al. 2010) and involve the extraction of analytes, utilising vortex or shaking in acetonitrile/buffers. Besides developing methods for residues determination in animal tissues, methods to confirm the residues are necessary. Because of the excellent sensitivity and selectivity of the mass spectrometry (MS), it is recognised as one of the best techniques for the confirmation of residues in animal tissues (European Commission 2002). There are several techniques available for determination of dyes in animal tissues: liquid chromatography–tandem mass spectroscopy (LC-MS/ MS) using a positive ion in electrospray ionisation mode (ESI) (Dowling et al. 2007; Halme et al. 2007; Stubbings et al. 2008; Tarbin et al. 2008), chemical ionisation (CI) without discharge of atmospheric pressure (APCI) with a tool for ion-trap (Turnipseed et al. 2005), LC–tandem mass spectrometry (Scherpenisse and Bergwerff 2005), LC–vis/FLD (visible and fluorescence detection; Mitrowska et al. 2005; Arroyo et al. 2008).

Food Additives and Contaminants The National Program for Control of Residues and Contaminants in Meat (beef, poultry, swine and equine), Milk, Honey, Eggs and Fish – PNCRC (Brasil 2009), establishes the monitoring of dyes in shrimp by LC-MS/MS, and the value of a reference limit of 2.0 mg kg–1 for regulatory actions. This value is the same as established by the EU for the minimum required performance limit (MRPL) for the sum of residues of MG and LMG in aquaculture products (European Commission 2002, 2004).

Materials and methods Chemicals and reagents All the solvents used were HPLC grade, and the chemicals were of analytical-reagent grade unless otherwise stated. MG (CAS 2437-29-8) and LMG (CAS 129-73-7) were obtained from Sigma, CV (CAS 548-62-9) from Acros and LCV (CAS 603-48-3) from Aldrich. Ammonium acetate buffer was prepared at 0.05 mol L–1 and the pH was adjusted to 4.5 with glacial acetic acid. Citrate buffer was prepared by dissolving citric acid mono hydrate and sodium chloride in approximately 700 mL of water. The pH was adjusted to 4 with sodium hydroxide solution at 10 mol L–1 and made up to 1 L. Solutions of acetonitrile/ammonia solution at 35% (95:5 v/v), acetonitrile/ acetic acid (95:5 v/v) and ethylene glycol/methanol (10:90 v/v) were prepared. Solid-phase extraction cartridges, SCX 500 mg/2.8 mL, were purchased from VARIAN and inorganic membrane filtration pore size (0.2 mm) from Whatman.

Standard and reagent solutions Standard solutions of MG, LMG, CV and LCV (1 mg mL–1) were prepared in methanol and stored at 20 C. The pool of intermediate solution (c ¼ 5.0 mg mL–1) and fortification solution (c ¼ 0.02 mg mL–1) were prepared on the day of use.

Blank sample Shaved salmon samples were obtained in a local supermarket, cut into small pieces and stored below 10 C in plastic bags. The shrimp samples were obtained from the Federal Service Inspection, ground without the shell and head and stored at lower than 10 C in plastic bottles. The samples were previously analysed with the methodology, and gave negative results for the analytes.

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Sample preparation Salmon and shrimp samples (2.0 g) were weighed in 50-mL centrifuge tubes and fortified with an addition solution containing the MG, LMG, CV and LCV dyes. After fortification of the samples, 4 mL of citrate buffer and 20 mL of acetonitrile were added in the same tube, homogenised for 1 min and centrifuged for 10 min. The supernatant was transferred to a 250-mL separating funnel, containing 1.5 g sodium chloride, and 10 mL of dichloromethane was added followed by 1 min stirring. The lower aqueous phase was discarded and the organic phase evaporated in rotavapor. The residue was re-constituted with 5 mL of acetonitrile–acetic acid (95:5 v/v) and transferred to a Bond Elut SCX cartridge pre-conditioned with 5 mL of acetonitrile–acetic acid (95:5 v/v). The extract was left in a drain flow from 1 to 3 mL min–1. Therefore, the cartridge was washed with 2.5 mL of acetone, 5 mL of methanol and 5 mL of acetonitrile. Fifty microliters of ethanedial–methanol (10:90) was added in test tubes to collect the eluate. The sample cartridge was eluted with 8 mL solution of acetonitrile–ammonia solution to 35% (95:5 v/v). Eluate was evaporated to almost dryness in water bath. The residue was reconstituted in 1 mL with a solution of ammonium acetate 0.05 mol L–1, pH 4.5, and acetonitrile (1:1) agitated in vortex for 15 s and left in an ultrasonic bath for 5 min. The sample was filtered through an inorganic membrane filtration pore size (0.2 mm) for a 2-mL flask and analysed by HPLC/MS/MS using a C18 column coupled with a lead (IV) post-column.

Apparatus The LC/MS/MS system was an Agilent model 1200 coupled to an API 5000 triple quadrupole of Applied Biosystems (MDS Sciex, Concord, Ontario, Canada). The acquisition and data processing was performed with Analyst software version 1.4.2.

HPLC analysis A C18 (5 mm particle size, 150 mm 2.0 mm i.d.) column (ACE) coupled to a post-column (10 mm 2 mm i.d.) packed with lead (IV) oxide, temperature 30 C, was used with mobile phase at 0.4 mL min–1 flow rate. The gradient for chromatographic separation (Table 1) of the analytes was performed using ammonium acetate 0.05 mol/acetonitrile (35:65) (mobile phase A) and ammonium acetate 0.005 mol/acetonitrile (20:80) (mobile phase B). LC gradient started with 100% of A, which was held to 5 min and was linearly increased to 100% of B; the mobile phase composition was maintained at 100% of A for 9 min. The re-equilibration time was 5 min. Injection volume was 20 mL.

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Table 1. Gradient used in liquid chromatography to determine the MG, LMG, CV and LCV.

Time (min)

Mobile phase A Ammonium acetate 0.05 mol/acetonitrile (35:65) v/v

Mobile phase B Ammonium acetate 0.05 mol/acetonitrile (20:80) v/v

100 100 0 0 100 100

0 0 100 100 0 0

0 5 6 15 16 20

Decision limit (CC ) and detection capability (CC )

Note: MG, malachite green; LMG, leucomalachite green; CV, crystal violet; leucocrystal violet.

Table 2. Values of the transitions monitored, optimised energies of declustering potential (DP), entrance potential (EP) and collision cell exit potential (CXP).

Analyte Malachite green Crystal violet Leucomalachite green Leucocrystal violet

residual form that prevails in tissues of aquatic animals treated with dyes, is oxidised to the precursor ion and thereby increases the sensitivity of the method for these forms (Halme et al. 2007). Therefore, the monitored transitions for leuco forms are the same as those of the precursor ion form, differentiating themselves from one another by their retention times.

Monitored transition

DP (V)

CE (V)

CXP (V)

329/208* 329/165 372/356* 372/340 329/208* 329/165 372/356* 372/340

160 160 165 165 160 160 165 165

51 88 57 74 51 88 57 74

20 20 20 20 20 20 20 20

Notes: MG, malachite green; LMG, leucomalachite green; CV, crystal violet; leucocrystal violet. *Transition used for quantitation

MS/MS conditions The analysis was performed using positive-ion electrospray (ESIþ) interface with multiple reaction monitoring (MRM) mode. Two transitions were monitored for each analyte, as shown in Table 2.

The decision limit (CC ) and detection capabilities (CC ) were calculated according to ISO 11843 (1997), as described in the Commission Decision 2002/657/EC. The determination of these parameters was obtained by the analysis and extraction of 5 blank samples fortified at levels of concentrations 0.50 MRPL, 0.75 MRPL, 1.00 MRPL, 1.25 MRPL and 1.50 MRPL. The blank sample was also analysed without fortification. The samples were injected in triplicate and this experiment was repeated twice. Linearity The study of linearity consisted of triplicate injections of standard solutions at concentrations 0.50 MRPL, 0.75 MRPL, 1.00 MRPL, 1.25 MRPL and 1.50 MRPL on 3 different days. The linearity was evaluated using the correlation coefficient (r) of the line by Student t test at 99% significance. Repeatibility/reproducibility The repeatability of the method was determined by the extraction of 18 replicates of shrimp at the following concentration levels: 6 blank samples fortified at 1.0 mg kg–1, 6 blank samples fortified at 2.0 mg kg–1, 6 blank samples fortified at 3.0 mg kg–1. This experiment was repeated twice. Reproducibility was the same test as repeatability but made with another analyst. Selectivity

Method validation The validation of the method and the estimate of the parameters of the values of the limit of decision (CC ), the capability of detection (CC ), linearity, repeatability/reproducibility, selectivity, matrix effect, method uncertainty and inter-species effect for the proposed methodology were determined in accordance with NBR/ISO/IEC/17025-2005 (ISO/IEC 2005) and the validation criteria described in Decision 657/2002 of the European Community (European Commission 2002). The minimum required performance limit was the 2 mg kg–1. The lead (IV) oxide post-column was used in the validation, because the leuco form, which is the

To establish the selectivity of this method, 6 replicates were analysed at levels 0.5 MRL, 1.0 MRL and 1.5 MRL totaling 18 analysed samples. Besides the studied analytes, a mixture of sulfonamides (sulfamethazine, sulfadimethoxine, sulfathiazole and sulfaquinoxaline at a concentration of 0.25 mg kg–1) to 3 replicates of each level was added. Matrix effect The matrix effect was assessed in shrimp by comparison of the slope of the calibration curve for standards with the calibration curve slope for fortified extracts of blank matrix, at the same levels of concentrations of standards (1.0, 1.5, 2.0, 2.5 and 3.0 mg kg–1).

Food Additives and Contaminants Statistical tests F (Snedecor) and t (Student) were used to assess the results. A calibration curve was also prepared with the same levels of concentration. The samples were injected in triplicate and the experiment was repeated twice. Method uncertainty The within-laboratory reproducibility (intermediate precision) and the calibration curves were used to estimate the combined measurement uncertainty. The calibration curve uncertainty and the intermediate precision were calculated and used to obtain the combined measurement uncertainty and multiplied by a factor (k ¼ 2) to obtain the expanded uncertainty. The choice of the factor k was based on the level of confidence desired. For this experiment, the level of confidence was 95%, so k is equal to 2 (Ellison et al. 2000). The estimation of uncertainty followed a simplified approach where the components evaluated in the study were the uncertainty in the regression (uncertainty of variables: slope m and intercept b of the calibration curve and the covariance between these two variables) and the uncertainty of precision, both in terms of reproducibility when analysing a 3-day study, three levels of concentration, a total of 54 samples extracted by the analyst. Data were collected from 2 analysts or 108 samples taken. Inter-species effect To study the inter-species effect, blank samples of shrimp and fish were weighed, and spiked at levels of 0.50 MRPL, 0.75 MRPL, 1.00 MRPL, 1.25 MRPL and 1.50 MRPL. The sample were extracted and injected in triplicate on 3 different days and then compared with the calibration curves which were obtained.

Results and discussion The mass spectrometer was optimised in the MRM mode by infusion of a solution of each analyte, at a concentration of 1.0 mg kgmL–1 in methanol–water (1:1 v/v) at a flow rate of 10 mL min–1. A quantitative analysis of MG, LMG, CV and LCV was performed in MRM mode by monitoring three transitions for each compound, after the optimisation, thus providing confirmation of results. After optimisation of the MRM mode by infusion, the ionisation conditions were established at a flow of 400 mL min–1, followed by the conditions of elution of the compounds in C18 column using lead (IV) oxide post-column as described earlier. Figure 1 shows a chromatogram of a sample fortified with MG, CV, LCV and LMG at a concentration of 2 mg kg–1.

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In this study, an equation of rectilinear calibration (y ¼ mx þ b) was used to fit the calibration data by the method of weighted least squares at five concentration levels: 1.0, 1.5, 2.0, 2.5 and 3.0 ng mL–1; this curve was used for quantification of analytes in recovery studies. The criterion for acceptance for the quality of the fitness was made by Student t test, and the regression coefficient (R2) of all calibration curves for each analyte are shown in Table 3. The linearity measured by Student t-test indicated the correlation coefficient (r) of the line obtained in the range from 0 to 3 mg kg–1. The test result indicated that the range studied was linear. The decision limit (CC ) and detection capability (CC ) were calculated following the ISO 11843 (1997) Directive, and the values obtained are indicated in Table 5. Values for CC were between 0.161 and 0.860 mg kg–1 and for CC between 0.218 and 1.162 mg kg–1. The recovery values for the repeatability, were in the acceptable range (% of the acceptable range of recovery between 50% and 120% for concentrations less than or equal to 1 mg kg–1 and 60% to 120 % for concentrations up to 10 mg kg–1) required by Directive 2002/657/EC (European Commission 2002). The values of CV% considered acceptable for precision (Codex Alimentarius Commission 2003) are 35% for concentrations 1 mg kg–1 and 30% for concentrations between 1 and 10 mg kg–1. By the data analysis (Table 4), the method can be considered accurate, except for LCV, which had a value above (31.6%) at a concentration of 3.0 mg kg–1. In this case, 33% of the values that build up the overall average are below the allowed average (60%), causing a large data variability reflecting the value of CV%. Through these data, it was observed that LCV solution did not exhibit good stability, thus it is necessary to prepare (the pool of intermediate solution and addition solution) the same day of use. According to the Codex Alimentarius Commission (FAO/WHO) (2009), the acceptable range of CV% for reproducibility experiments is 53% for concentrations 1 mg kg–1 and up to 45% for concentrations between 1 and 10 mg kg–1. The comparison of these two experiments (intermediate precision) confirmed the accuracy and precision of the method used to determine the MG, LMG, CV and LCV in salmon and shrimp. The results of the selectivity study showed that the addition of possible interferences did not give different peaks than those that were monitored in the chromatograms of the samples contaminated with interfering compound. The matrix effect was obtained comparing the responses of the curves of direct standards with those of fortified blank samples. The statistical tests F (Snedecor) and Student t were used to assess the

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Figure 1. Chromatogram of sample added to malachite green, crystal violet, leucomalachite green and leucocrystal violet in 2 mg kg–1 concentration.

Table 3. Values of the equation (y ¼ mx þ b) obtained through weighted least squares in linear range from 1 to 3 mg L–1. Analyte Malachite green

Crystal violet

Leucomalachite green

Leucocrystal violet

Day 1 2 3 1 2 3 1 2 3 1 2 3

Slope m

Intercept b

164,955.238 3247.619 0.977 182,915.138 258.878 0.989 185,381.905 11,552.381 0.985 215,382.857 2321.429 0.983 178,335.238 6647.619 0.960 158,104.762 2302.381 0.951 309,675.238 319.048 0.970 339,820.952 10,473.810 0.985 370,879.048 6026.190 0.987 331,129.524 6945.238 0.989 90,422.095 4177.381 0.986 352,416.190 4204.762 0.984

results, which confirmed the matrix effect, with the same result when the shrimp sample was added in the procedure. The uncertainty of measuring volume and mass were assessed previously, and found to make a

negligible contribution to the overall uncertainty of the method. The equipment for measuring the volume and mass in the laboratory are calibrated, checked periodically and used in a random order. The influence of the calibration components and precision under reproducibility conditions could not be incorporated, resulting in simplification of the estimation procedure. The results are given in Table 5 for the analytes studied, the concentration 2.0 mg kg–1. The study of the inter-species matrix effect was performed through comparison of calibration curves of fortified blank samples of shrimp with the curves for fish. The results were compared using the test of F (Snedecor) and the t-test (Student) in three different days, giving matrix inter-species. The quantification of real samples must be carried out using a calibration curve of the same species.

Conclusions The validation of the method was performed according to the method proposed by the DEFRA (2006) and

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Table 4. Recovery values and variation coefficient of the sample of shrimp. Average recovery (%) Repeatability Analyte (more intense transition)

Concentration Average of (mg kg–1) 3 days (%)

Malachite green 329/208

1.0 2.0 3.0 1.0 2.0 3.0 1.0 2.0 3.0 1.0 2.0 3.0

Crystal violet 374/356

Leucomalachite green 329/208

Leucocrystal violet 374/356

67.6 68.7 69.2 105.9 89.5 83.4 84.5 75.6 69.8 64.1 63.8 65.2

Table 5. Decision limit (CC ), detection capability (CC ) and uncertainty (at 2.0 mg kg–1) of MG, LMG, CV and LCV dyes in shrimp.

Analyte MG (329/208) CV (372/356) LMG (329/208) LCV (372/356)

CC mg kg–1

mg kg–1

Expanded Uncertainty mg kg–1

0.164 0.248 0.161 0.860

0.222 0.335 0.218 1.162

2.0 0.209 2.0 0.775 2.0 0.758 2.0 0.284

Note: MG, malachite green; CV, crystal violet; LMG, leucomalachite green; LCV, leucocrystal violet.

executed to meet the NBR/ISO/IEC/17025-2005 and the validation criteria described in Decision 657/2002 of the European Community. The validated method was, accurate, precise and selective in the simultaneous analysis of MG, CV and their metabolites in fish and shrimp tissue. The LC-MS/MS analysis by electrospray in a positive ion mode was used as an efficient and suitable alternative for determination of MG, CV and its metabolites in shrimp and fish. The use of a triple quadrupole mass spectrometer in MRM mode enabled low limits of CC and CC to be achieved, and the findings demonstrate the suitability of proposed analytical method to detect residues of MG, CV and its metabolite (LMG and LCV) in aquatic species at low residue levels.

References Andersen AC, Roybal JE, Turnipseed SB. 2005. Liquid chromatographic determination of malachite green and leucomalachite green (LMG) residues in salmon with in

s 10.6 7.9 6.6 28.5 9.0 9.4 21.8 13.0 14.1 16.8 15.7 20.6

Reproducibility CV Average of (%) 3 days (%) 15.6 11.5 9.5 26.9 10.0 11.3 25.9 17.2 20.1 26.2 24.7 31.6

68.1 69.7 74.3 114.6 104.7 98.8 93.5 84.9 78.9 76.9 80.8 76.8

Intermediate precision

CV (%)

Average of 3 days (%)

CV%

11.7 6.4 8.0 20.0 20.5 27.9 9.0 9.1 14.8 17.5 8.8 19.2

17.1 9.1 10.8 17.4 19.6 28.2 9.6 10.7 18.8 22.8 10.9 25.1

67.9 69.2 71.7 110.3 97.1 91.1 88.9 80.3 74.4 70.5 71.8 70.8

11.0 7.1 7.7 24.7 17.4 21.9 17.2 12.0 15.0 18.1 15.4 20.5

16.2 10.2 10.7 22.4 17.9 24.1 19.4 15.0 20.1 25.7 21.4 29.0

situ LMG oxidation. J Assoc Off Analyt Chem Int. 88:1292–1298. Arroyo D, Ortiz MC, Sarabia LA, Palaćios F. 2008. Advantages of PARAFAC calibration in the determination of malachite green and its metabolite in fish by liquid chromatography-tandem mass spectrometry. J Chromat A. 1187:1–10. Brasil, Ministe´rio da Agricultura, Pecua´ria e Abastecimento. 2009. Instruçaõ Normativa No 14, de 25 de maio de 2009. Dia´rio Oficial da Uniaõ de 28 de maio de 2009, Seçaõ 1, pa´gina 28. Chen G, Miao S. 2010. HPLC determination and MS confirmation of malachite green, gentian violet, and their leuco metabolite residues in channel catfish muscle. J Agric Food Chem. 58:7109–7114. Codex Alimentarius Commission. 2003. Report of the twenty-sixth – Codex Committee on Residues of Veterinary Drugs in Food, ALINORM 03/23. Codex Alimentarius Commission (FAO/WHO). 2009. Appendix V, Guidelines for the design and implementation of National Regulatory Food Safety Assurence Programmes associated with the use of veterinary drugs in food producing animals. ALINORM 09/32/31. DEFRA. 2006. Central Science Laboratory. Determination of basic drugs, including malachite green, at residues levels using multiresidue extraction and clean-up procedure. Issued by D. Tyler (Technical manager, VM). Method 22(7). Revision and reviewed: 24 February 2006. Dowling G, Mulder PPJ, Duffy C, Regan L, Smyth MR. 2007. Confirmatory analysis of malachite green, leucomalachite green, crystal and leucocrystal violet in salmon by liquid chromatography-tandem mass spectrometry. Anal Chim Acta. 586(1–2):411–419. Ellison SLR, Rosslein M, Williams A, editors. 2000. Eurachem/CITAC Guide CG4, Quantifying uncertainty in analytical measurement. 2nd ed. UK: Eurachem/ CITAC.

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European Commission. 2002. Commission Decision 2002/ 657/EC of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off J Eur Commun. L221. European Commission. 2004. Commission Decision 2002/ 657/EC of 22 December 2003 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off J Eur Union L6. Hall Z, Hopley C, O’Connor G. 2008. High accuracy determination of malachite green and leucomalachite green in salmon tissue by exact matching isotope dilution mass spectrometry. J Chromat B. 874:95–100. Halme K, Lindfors E, Peltonen K. 2007. A confirmatory analysis of malachite green residues in rainbow trout with liquid chromatography – electrospray tandem mass spectrometry. J Chromat B. 845:74–79. ISO 11843. 1997. Capability of detection – Part 1: terms and definitions, Part 2: methodology in the linear calibration case. Geneva, Switzerland. ISO/IEC. 2005. ISO/IEC 17025:2005: general requirements for the competence of testing and calibration laboratories. Geneva, Switzerland. Martı´ nez MJ, Herrera S, Ucle´s A, Agu¨era A, Hernando MD, Shimelis O, Rudolfsson M, Ferna´ndez-Alba AR. 2010. Determination of malachite green residues in fish using molecularly imprinted solid-phase extraction followed by liquid chromatography-linear ion trap mass spectrometry. Analytica Chimica Acta. 665:47–54. Mitrowska K, Posyniak A, Zmudzki J. 2005. Determination of malachite green and leucomalachite green in carp muscle by liquid chromatography with visible and fluorescence detection. J Chromat A. 1089:187–192. Mittelstaedt RA, Mei N, Webb PJ, Shaddock JG, Dobrovolsky VN, McGarrity LJ, Morris SM, Chen T, Beland FA, Greenlees KJ, et al. 2004. Genotoxicity of

malachite green and leucomalachite green in female Big Blue B6C3F1 mice. Mutat Res. 561:127–138. Rushing LG, Thompson HC Jr. 1997. Simultaneous determination of malachite green, gentian violet and their leuco metabolites in catfish or trout tissue by high-performance liquid chromatography with visible detection. J Chromat B. 688:325–330. Scherpenisse P, Bergwerff AA. 2005. Determination of residues of malachite green in finfish by liquid chromatography tandem mass spectrometry. Analytica Chimica Acta. 529:173–177. Stammati A, Nebbia C, De Angelis I, Albo AG, Carletti M, Rebecchi C, Zampaglioni F, Dacasto M. 2005. Effects of malachite green (MG) and its major metabolite, leucomalachite green (LMG), in two human cell lines. Toxicol In Vitro. 19:853–858. Stubbings G, Tarbin J, Cooper A, Sharman M, Bigwood T, Robb P. 2008. A multi-residue cation-exchange clean-up procedure for basic drugs in produce of animal origin. Anal Chim Acta. 547:262–268. Tarbin JA, Chan D, Stubbings G, Sharman M. 2008. Multiresidue determination of triarylmethane and phenothiazine dyes in fish tissues by LC-MS/MS. Anal Chim Acta. 625:188–194. Turnipseed SB, Andersen WC, Roybal JE. 2005. Determination and confirmation of malachite green and leucomalachite green residues in salmon using liquid chromatography/mass spectrometry with no-discharge atmospheric pressure chemical ionization. J Assoc Off Analyt Chem Int. 88:1312–1317. Wang L, Du X, Zhao Y, Liu Y. 2010. Rapid residue determination of malachite green, crystal violet and their metabolites in aquatic products by UPLC-MS/MS. South China Fisheries Sci. 6:32–36. Yuan JT, Liao LF, Xiao XL, He B, Gao SQ. 2009. Analysis of malachite green and crystal violet in fish with bilinear model. Food Chem. 113:1377–1383.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 609–616

Development and validation of a method for Cd, Pb and As analysis in bovine, equine and poultry liver by inductively coupled plasma mass spectrometry P.C.P. Laraab*, H.J.F. Fabrinoa, A. Germanoa and J.B.B. Da Silvab a

Ministry of Agriculture, Livestock and Supply – National Agricultural Laboratory (LANAGRO), Pedro Leopoldo, MG, Brazil; Departamento de Quı´mica, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

(Received 23 November 2010; final version received 19 July 2011) A method for the determination of As, Cd and Pb in bovine, equine and poultry liver by ICP-MS was developed and validated. Samples were digested in a microwave oven using a 10% HNO3 solution. A set of experiments was made according to a central composite design (CCD) for optimisation of the plasma argon flow, nebuliser argon flow and radiofrequency power applied to the plasma. During the validation, Rh and Ru were evaluated as internal standards and, after validation, the best was Rh for Pb and Cd analysis, but for As better results were obtained without an internal standard. The method allowed As, Cd and Pb determination with a 3.3% HNO3 solution for the calibration curves ranging from 0 to 40 mg l 1 for As and from 0 to 20 mg l 1 for Cd and Pb. The recovery values obtained showed averages of 100%, 106% and 96% for As, Cd and Pb, respectively. Limits of quantification obtained were 85 mg kg 1 for As, 6.5 mg kg 1 for Cd and 12.5 mg kg 1 for Pb. Repeatability and within-laboratory reproducibility were evaluated through the indicators HORRATr and HORRATR, and the results were less than 0.30. The method is simple, fast and showed adequate precision and accuracy for the determination of As, Cd and Pb in bovine, equine and poultry liver. The precision, recovery, uncertainties, and limits of detection and quantification for each analyte were in accordance to European Union Commission Regulation 2007/333/EC. Keywords: ICP/MS; metals analysis – ICP/MS; in-house validation; heavy metals – arsenic; heavy metals – cadmium; lead; meat; animal products – meat

Introduction Aiming to protect human health and ensure the quality of food, the Brazilian Ministry of Agriculture, Livestock and Supply (MAPA) is responsible for the development and execution of the National Residue and Contaminants Control Plan (PNCRC) (Mauricio et al. 2009), in plant products and animal areas. This programme is a key piece in the export of Brazilian agricultural products to the European Union, the United States, Canada, Russia, China and other international markets. An important component of any food safety programme is the control and monitoring of residues and contaminants. This monitoring, in turn, depends directly on laboratory testing using valid and reliable analytical methods. Some international regulations prescribe the validation of methods suitable for official control of contaminants in food. Decision 2002/657/EC (European Commission 2002) establishes performance criteria and other requirements for analytical methods. These requirements include the handling of samples and procedures for the validation of methods, such as

*Corresponding author. Email: paulocpl@gmail.com ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.608381 http://www.tandfonline.com

the need to validate a method in concentrations around the maximum residue limit (MRL). Regulation 2006/ 1881/EC (European Commission 2006) sets MRLs for various types of contaminants and various matrices. In that document the MRL for Cd and Pb in equine, poultry and bovine liver is set at 0.5 mg kg 1, with no MRL established for As, but Ordinance No. 11 published in the Official Journal of Brazil (BRASIL 2004) fixed this value at 1.0 mg kg 1. Regulation 2007/333/EC (European Commission 2007) sets out criteria for assessing the conformity of an analytical method, where there are specified criteria for the acceptance of precision, recovery, specificity and limits of detection and quantification for methods for cadmium, lead, inorganic tin and mercury analysis. In the case of Cd and Pb the LOD should be less than onetenth of the MRL and quantification, less than onefifth of the MRL. For the accuracy values HORRATr and HORRATR must be less than 2 and the method cannot have matrix or spectral interferences. We adopt the same criteria for the As, since there is no specification in the legislation for this analyte.

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There are some publications concerning the analysis of bovine liver. Friese et al. (2001) used electrothermal vaporisation with inductively coupled plasma mass spectrometry (ETV-ICP-MS) for the determination of As, Cd, Cu, Fe, Mn, Pb and Zn in reference biological materials such as BCR 185R Bovine Liver. In another study, Ward et al. (1990) made a comparison between the pneumatic nebulisation and laser ablation sample introduction systems for the analysis of Li, Mg, Al, Ca, Cr, Mn, Fe, Cu, Zn, Br, Rb and Cs in some reference certified materials, including IAEA Mixed Human Diet (H9), NBS SRM 909 Human Serum and NBS Bovine Liver SRM 1577a. In method validation of Cd and Pb in offal a more detailed study was published by Forte and Bocca (2007). In this publication a method for the determination of Cd and Pb in bovine, ovine and porcine offal (which have different MRLs) by sector field inductively coupled plasma mass spectrometry (SF-ICP-MS) was developed and validated. They used 2 g of sample and 6 ml of a 5:1 (v/v) mixture of super-pure 67% HNO3 and suprapur H2O2 to digest the samples in a microwave oven system. The authors do not consider the different MRLs in the method validation as recommended by Decision 2002/657/CE. Therefore, only a linearity range covering all MRL was used for validation. To ensure better accuracy and precision at concentrations close to the value for a decision on the infringement of the sample and to meet Decision 657/2002/EC, the method proposed here was validated around the MRL. Each analyte–matrix combination has a defined MRL, and coincidences of MRLs can occur for more than one analyte–matrix combination. Thus, in this work a method for As, Cd and Pb determination in bovine, equine and poultry liver was developed and validated according to international regulations, because these analytes–matrix have the same MRL and these matrices are similar (Codex Alimentarius Committee (CAC) 2003). The samples were determined by ICP-MS after a microwave-assisted digestion procedure. Mass spectrometry is an instrumental analytical technique that allows the separation of ionic species by the charge-to-mass ratio (Skoog et al. 2002). Inductively coupled plasma mass spectrometry (ICPMS) is a very sensitive and multi-element technique that is efficient in determining the elemental content in different kinds of sample (Silveira et al. 2007). In method development a central composite design was employed in order to select a potential internal standard and to optimise the ICP conditions. The validation procedures includes linearity, matrix effects studies, precision, accuracy, robustness, detection and quantification limits, capability of decision (CC ) and selectivity. Besides, the correct combination of analytical results with their respective uncertainties allows the correct comparison of analytical data between different

laboratories and the evaluation of the method with its purpose. There are basically two strategies for the estimation of uncertainty: one advocated by ISO/IEC 98 (International Organization for Standardization (ISO) 1995) based on a combination of all sources of uncertainty through the law of uncertainty propagation (bottom-up); the other called top-down, which uses data validation of the method, quality control data of long-term and collaborative tests for reproducibility of the analytical method. In this study the uncertainty in the analytical concentration was obtained from two main sources of uncertainty: reproducibility and calibration uncertainties, in a composition of top-down with bottom-up strategies (Oliveira 2009).

Materials and methods Materials and reagents All solutions were prepared using ultrapure deionised TM Element system (Millipore, water in a Milli-Q Bedford, MA, USA). Before use, all laboratory ware was thoroughly cleaned, kept in a 5% (v/v) nitric acid bath for more than 24 h and rinsed several times with ultrapure deionised water. High purity nitric acid of 65% (v/v) was obtained in a DuoPur Acid Purification System (Milestone, Bergamo, Italy) from pure HNO3. NIST traceable ICP-MS solutions from Fluka (TraceCERTTM Ultra) of Pb, Cd stock solutions 997 2 mg l 1, As stock solution 994 2mg l 1 and Rh, Ru, Ir, Pd and Y (Titrisol – Merck) 1000 1mg l 1 were used. Varian tuning solution containing 5mg l 1 of Ba, Be, Ce, Co, Pb, Mg, Tl and Th were used to calibrate the mass analyser. Certified Reference Material Bovine Liver 185R (IRMM, Geel, Belgium) was used to check the accuracy of the method.

Apparatus A model Varian 820-MS quadrupole inductively coupled plasma mass spectrometer (Varian, Australia) was used for all measurements. The instrument was equipped with a microflow nebuliser, a quartz Scott double-pass spray chamber, a quartz torch and the Varian SPS3 autosampler. Argon, 99.999% purity (White Martins, MG, Brazil), was also used. The total metal content was determined in the samples after a microwave-assisted digestion in a Multi-wave 3000 oven (Anton Paar, Austria) in quartz tubes with subboiled HNO3 10% (v/v) solution.

Sampling and sample preparation According to CAC (2003) validation data using representative matrix species can be extended to other similar species. Therefore the present work uses bovine

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Table 1. Matrix of experiments for the optimisation of some instrumental parameters and choice of internal standards. Experiment number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 (n ¼ 5)

Plasma argon flow (l min 1)

Nebuliser argon flow (l min 1)

RF power (kW)

14 20 14 20 14 20 14 20 12 22 17 17 17 17 17

0.8 0.8 1.2 1.2 0.8 0.8 1.2 1.2 1.0 1.0 0.7 1.3 1.0 1.0 1.0

1.2 1.2 1.2 1.2 1.6 1.6 1.6 1.6 1.5 1.5 1.5 1.5 1.0 1.6 1.5

liver to validate a method for As, Cd and Pb analysis in bovine, equine and poultry liver. About 1 kg of liver samples from animals on Brazilian farms was carefully treated. Visible fat and connective tissues were removed and the samples were chopped, ground and homogenised into a stainless-steel blender and kept at 18 C until analysis. Before use, the samples were allowed to reach room temperature. About 0.3 g of raw bovine liver were weighed and digested in a microwave oven using 5 ml of 10% (v/v) HNO3 solution. The irradiation programme for eight quartz tubes consisted of two steps: 10 min to reach 1300 W (ramp) and 20 min at 1300 W (hold). After cooling (approximately 20 min) the digested samples were quantitatively transferred into polypropylene tubes and diluted to 15 ml with ultrapure deionised water.

Results and discussion Optimisation of instrumental parameters and choice of internal standards (IS) For optimisation, a bovine liver sample with low analytes concentrations spiked with 5mg l 1 of each analyte and 3mg l 1 of each possible internal standard was used. Starting from the recommended conditions by the ICP-MS manufacturer, a set of 19 experiments was carried out by varying plasma argon flow, nebuliser argon flow and RF power applied to the plasma following a central composite design (Table 1) to choose a metal, between 103Rh, 101Ru, 193Ir, 105Pd and 89Y, which fits better as an internal standard. At the same time the conditions that showed the best detection limits for the three analytes were adopted as optimal. The results were plotted as counts per second (cps) versus the number of the experiment (Figure 1)

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and only Pd did not have a similar behaviour to the analytes when varying the experimental conditions. The conditions of experiment 15 were considered optimal because they showed better sensitivity for all analytes; the metals Rh, Ru, Ir and Y were selected as potential internal standards. Thereby, the analysis conditions were as shown in Table 2, and during the method validation the metal that showed best results was chosen as an internal standard.

Method validation According to Decision 2002/657/EC the calibration curves must be prepared around the MRL. Therefore, all calibration curves were built with six points around the MRL (0, 0.4, 0.8, 1.2, 1.6 and 2.0 times the MRL) in a range of 0–2000 mg kg 1 for As and 0–1000 mg kg 1 for Cd and Pb, i.e. 0–40 and 0–20mg l 1 after sample preparation, respectively. To decide about the best mathematical model to use to calculate the linear regression, an F-test was performed to compare the highest with the lowest cps variance among the calibration curve points. When the cps variances were approximately equal (homoskedasticity) an unweighted regression calculation was used, but if the variances were different (heteroskedasticity) the data were treated by a weighted regression calculation (Miller and Miller 2005). The majority of the calibration curves required a weighted regression calculation and all gave R2 4 0.99 for the analysed isotopes. To evaluate the matrix effect, three calibration curves prepared from a bovine liver sample with low concentrations of the analytes spiked in five concentrations levels around the MRL and another three in HNO3 3.33% (v/v) were made. The results were evaluated using Rh, Ru, Y or Ir as an internal standard. Comparing the averages of the angular and linear coefficients of these curves through statistical tools (F- and Student’s t-tests) the results showed the presence of the matrix effect, at a 95% confidence level, only when Ir or Y was used as an internal standard for the analysis of Pb. As and Cd did not have matrix effects in any of the analyte–internal standard combinations. In an attempt to find only one internal standard to attend all the analytes, it was decided to evaluate only Ru and Rh as internal standards because they had no matrix effects for all analytes. The absence of an matrix effect reveals that the digestion of samples using dilute HNO3 was efficient. The precision of the method was determined through repeatability and within-laboratory reproducibility. Eighteen blank samples spiked in three concentration levels (0.5, 1.0 and 1.5 times the MRL) in groups of six independent replicates for each level were digested and analysed. For repeatability, the 18

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Figure 1. Behaviour of analytes and internal standards in the face of variations in plasma argon flow, nebuliser argon flow and RF power applied to the plasma.

Table 2. Optimal conditions for ICP-MS analysis of bovine, equine and poultry liver. Parameters Gas flow (l min 1)

Plasma flow Auxiliary flow Nebuliser flow Sheath flow

RF (kW) RF power Sample introduction Sampling depth (mm) Pump rate (rpm) Stabilisation time (s) Spray chamber ( C) Ion optics (volts)

Quadrupole scan

First extraction lens Second extraction lens Third extraction lens Corner lens Mirror lens left Mirror lens right Mirror lens bottom Entrance lens Entrance plate Fringe bias Pole bias Scan mode Dwell time (ms) Points per peak Scans/replicate Replicates/sample

Settings 17.0 1.70 1.00 0.18 1.50 5.50 4 20 3 30 250 294 280 35 20 40 4 40 3.2 0 Peak hopping 1000 2 10 3

samples were prepared three times (once a day) on 3 different days by the same analyst using the same experimental conditions (same sample, reagents and standards). For within-laboratory reproducibility, another two analysts performed the 18 samples on different days, once each. According to Regulation 2007/333/EC, the maximum limit to precision is given by the HORRATr value for repeatability and HORRATR for within-laboratory reproducibility. HORRAT values are defined as the ratio between the experimental relative standard deviation (RSD) and the RSD calculated by the Horwitz equation: RSD ð%Þ ¼ 21 0:5 log C where C is the concentration at the investigated level. For inorganic contaminants the maximum HORRATr and HORRATR values are 2 (European Commission 2007). In the proposed method the HORRATr and HORRATR values were 50.3 for all investigated levels. Accuracy was checked throughout the use of CRM 185R Bovine Liver where Cd and Pb have certified values of 544 17 and 172 9 mg kg 1, respectively. It was determined to digest about 0.3 g of the CRM in triplicate. The concentrations for the CRM found were 582 17 and 162 6 mg kg 1 for Cd and Pb, respectively. Using a student’s t-test for 95% of confidence,

Food Additives and Contaminants the method was found to be accurate for both analytes. The concentration in CRM for As was below the limit of quantification (LOQ) of the method and so could not be evaluated. In addition, recovery studies were carried out in order to improve the accuracy evaluation and to check As accuracy. These studies were performed using the same samples prepared in precision studies, i.e. blank bovine liver spiked in three concentration levels made over 5 different days by three different analysts. For all analytes the recovery was close to 100%, showing the good accuracy of the method (Table 3). The limit of detection (LOD) and the LOQ were calculated using: LOD ¼ 3 S0 =b LOQ ¼ 10 S0 =b as recommended by Regulation 2007/333/EC, where S0 is the standard deviation for 21 measurements of the bovine liver blank; and b is the slope of the calibration curve. The LOD and LOQ were 25 and 85, 2 and 6.5, and 4 and 12.5 mg kg 1 for As, Cd and Pb, respectively. The LOD and LOQ were in accordance with Regulation 2007/333/EC, i.e. LOD 5 10% of MRL and LOQ ¼ 20% of the MRL. The limit of decision (CC ) is defined as a concentration that leads, for a given error probability , to the decision that the observed system is not in its basic state (ISO 1997). In our case, the basic state is the MRL, and the CC is a limit of concentration at which any value above it has a 95% confidence that the concentration is above the MRL. The limits of decision in this work were calculated by using ISO/IEC 11843 (ISO 1997) and the values found were 1075, 515 and 509 mg kg 1 for As, Cd and Pb determination, respectively. According to the results of precision, recovery, LOD, LOQ and CC (Table 3), it can be seen that the precision was appropriate (HORRAT 5 2) with all combinations of analyte–internal standard. Recovery was better when using Rh as an internal standard for Cd and Pb and no internal standard for As. LOQs were approximately the same for Cd and Pb, but for As it was better without internal standard. Thus, Rh was chosen as the best internal standard for Cd and Pb analysis and no internal standard for As analysis. Using Rh as internal standard to Cd and Pb, the method robustness was evaluated. Robustness is one requirement for the validation of an analytical method and is defined as ‘a measure of its capacity to remain unaffected by small, but deliberate variations in method parameters and provides an indication of its reliability during normal usage’ (EURACHEM 1998). Nebuliser argon flow, plasma argon flow, digestion time and radiofrequency power applied to plasma were evaluated through the approach of Youden (Youden

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and Steiner 1975; Bedregal et al. 2008). The Youden approach estimates the effect of each variable and compares the standards deviation of the experiments with the reproducibility of the method. If the standard deviation is less than reproducibility, the parameters do not affect the method. The matrix of experiments for robustness assessment is shown in Table 4. As regards As and Pb, the results show that only the nebuliser argon flow has a significant effect, and for Cd the time of digestion in a microwave oven has a significant effect. Therefore, these parameters must be strictly controlled in the method. As is susceptible to chloride interference because the Ar can combines with chloride forming a 40 Ar35Clþ ion which has a charge-to-mass ratio equal to 75Asþ. Selectivity was investigated by studying the ‘method ability to measure the analyte of interest in test portions to which specific interferences have been deliberately introduced’ (EURACHEM 1998). In this work only the polyatomic interference of ArCl in analyses of As was investigated. For this, an aqueous solution spiked with the As in a concentration equal to the MRL was prepared in triplicate and its signal was compared, through F- and student’s t-tests, with the signal of triplicates of the other aqueous solutions spiked with As, in the MRL concentration, and chloride, at a concentration of 0.29%. This concentration is the same of the standard reference material NIST 1577c and was adopted as a representative concentration of chloride in bovine livers. This chloride concentration does not interfere with the determination of As.

Method uncertainty The two sources of uncertainty that most contribute to the combined uncertainty of the analytical result is the uncertainty of the calibration of the measuring instrument (ucalib) and the reproducibility of the analytical method (urepro). In this work the combined uncertainty on the concentration of the analyte (uCanal) was obtained by combining the uncertainty of the calibration and reproducibility of the method by the equation: uCanal ¼ ½ðccalib ucalib Þ2 þ u2repro 1=2 The uncertainty of reproducibility was obtained from the within-laboratory reproducibility data; ccalib is a sensitivity coefficient obtained from the equation of the calibration curve written as a function of the analyte concentration interpolated from the calibration curve. This coefficient of sensitivity is necessary due to the fact that the units of concentration of the calibration curve may not be the same as the final result. This simplified methodology for the calculation of uncertainty is a composition of bottom-up and top-down strategies, and it does not include the uncertainty of

Matrix effect

As As/Rh As/Ru As/Ir As/Y Cd Cd/Rh Cd/Ru Cd/Ir Cd/Y Pb Pb/Rh Pb/Ru Pb/Ir Pb/Y

No No No No No No No No No No No No No Yes Yes

Concentration level

Signal evaluated

0.21 0.08 0.21 – – 0.36 0.21 0.28 – – 0.24 0.13 0.36 – –

0.5 LMR 0.20 0.09 0.20 – – 0.27 0.16 0.18 – – 0.20 0.09 0.23 – –

1.0 LMR 0.26 0.07 0.06 – – 0.35 0.18 0.14 – – 0.26 0.09 0.09 – –

1.5 LMR

Repeatability (HORRATr) (n ¼ 18)

0.24 0.23 0.59 – – 0.22 0.12 0.52 – – 0.25 0.18 0.63 – –

0.5 LMR 0.22 0.15 0.48 – – 0.24 0.29 0.52 – – 0.22 0.15 0.44 – –

1.0 LMR 0.18 0.11 0.15 – – 0.18 0.08 0.10 – – 0.27 0.17 0.13 – –

1.5 LMR

Within-laboratory reproducibility (HORRATR) (n ¼ 18)

99.8 113.6 95.7 – – 91.9 105.9 88.5 – – 83.6 95.0 76.7 – –

0.5 LMR 100.1 112.5 102.3 – – 93.4 107.1 97.6 – – 86.1 96.7 87.2 – –

1.0 LMR

99.7 112.2 110.9 – – 91.3 104.8 104.1 – – 86.0 96.5 95.8 – –

1.5 LMR

Recovery (n ¼ 30, %)

25 40 55 – – 1.5 2 2 – – 3.5 4 4 – –

Method LOD (mg kg 1)

85 135 190 – – 5.5 6.5 7.5 – – 12.5 12.5 14 – –

Method LOQ (mg kg 1)

1075 1075 1045 – – 539 515 522 – – 528 509 514 – –

CC (mg kg 1)

Table 3. Matrix effect, precision, recovery, limit of detection (LOD), limit of quantification (LOQ) and limit of decision (CC ) for As, Cd and Pb when evaluated with and without internal standards.

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Table 4. Design of experiments to evaluate the robustness of the method by the Youden approach. Parameter

Experiment

Nebuliser argon flow (l min 1) Plasma argon flow (l min 1) Digestion time (hold) (min) RF power (kW)

1.1 17.5 20 1.6

1.1 17.5 15 1.6

Table 5. Standard measurement uncertainties in the MRL concentration for the proposed method and maximum standard measurement uncertainties in accordance with Resolution 333/2007/EC.

Analyte As Cd Pb

Method standard uncertainty (mg l 1)

Maximum standard uncertainty (mg l 1)

1.33 0.57 0.51

2.02 1.20 1.20

1.1 16.5 20 1.4

1.1 16.5 15 1.4

0.9 17.5 20 1.4

0.9 17.5 15 1.4

0.9 16.5 20 1.6

0.9 16.5 15 1.6

of explosion. Second is the smallest amount of sample required in the analysis. Results obtained with the parameters of merit indicate that the proposed method presents adequate sensitivity, trueness (precision and accuracy), selectivity and robustness. Thereby, the method can be applied in the National Residues and Contaminants Control Plan for the control of As, Cd and Pb in bovine, equine and poultry liver.

Acknowledgments

sampling. Methods for the official control must produce results with uncertainties of standard measurement uncertainty below the maximum standard measurement (Resolution 2007/333/EC), calculated using the equation: Uf ¼ ½ðLOD=2Þ2 þ ð CÞ2 1=2 where C is the analytical concentration; and is a numerical factor that depends on C. The uncertainty in the concentration of the analytical method developed here calculated for the concentration corresponding to the MRL was below the maximum accepted by the legislation (Table 5) for all analytes.

Conclusions A simple method optimisation using a central composite design as a base of experiments was applied in order to choose potential internal standards and optimise the ICP conditions. The best internal standard was Rh for Cd and Pb analysis and none for As determination. On the optimised conditions the method was validated by evaluating the matrix effect, repeatability, within-laboratory reproducibility, recovery, robustness, selectivity and determining the limits of detection, quantification and decision. The uncertainty of the method was composed mainly from the contribution of the within-laboratory reproducibility and the uncertainty in the calibration curves in composition top-down and bottom-up approaches. There are two advantages to this method. First is the use of dilute nitric acid instead of concentrated acid, which can lead to a significant pressure increase inside the microwave oven vessels and increase the risk

The authors are thankful to the Conselho Nacional de Pesquisa e Desenvolvimento Tecnolo´gico (CNPq) for the scholarship supply and to the Brazilian Ministry of Agriculture, Livestock and Supply.

References Bedregal P, Torres B, Ubillu´s M, Mendoza P, Montoya E. 2008. Robustness in NAA evaluated by the Youden and Steiner test. J Radioanalyt Chem. 278(3):801–806. BRASIL. 2004. Portaria n 11 de 29 de janeiro de 2004 que aprova os programas para o Controle de Resı´ duos em carne, leite e pescado. Dia´rio Oficial da Unia˜o, No. 22(2 February), Brası´ lia (Brazil). Codex Alimentarius Committee (CAC). 2003. Guidelines on good laboratory practice in residue analysis. CAC/GL 401993, Budapest, Hungary, Rev. 1-2003. EURACHEM. 1998. The fitness for purpose of analytical methods: a laboratory guide to method validation and related topics, EURACHEM Working Group, Internet version. European Commission. 2002. Commission Decision 2002/ 654/EC of 12 August implementing Council Directive 96/ 23/EU concerning the performance of analytical methods and the interpretation of results. Brussels (Belgium): European Commission. European Commission. 2006. Commission Regulation 1881/ 2006/EC of 19 December setting maximum levels for certain contaminants in foodstuffs. Brussels (Belgium): European Commission. European Commission. 2007. Commission Regulation 333/ 2007/EC of 28 March laying down the methods of sampling and analysis for the official control of the levels of lead, cadmium, mercury, inorganic tin, 3-MCPD and benzo(a)pyrene in foodstuffs. Brussels (Belgium): European Commission. Forte G, Bocca B. 2007. Quantification of cadmium and lead in offal by SF-ICP-MS: method development and uncertainty estimate. Food Chem. 105:1591–1598.

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Friese KC, Grobecker KH, Wa¨tjen U. 2001. Development of an electrothermal vaporization ICP–MS method and assessment of its applicability to studies of the homogeneity of reference materials. Fresenius J Analyt Chem. 370(5):499–507. International Organization for Standardization (ISO). 1995. ISO/IEC Guide 98. Guide to the expression of uncertainty in measurement (GUM). Geneva (Switzerland): ISO. International Organization for Standardization (ISO). 1997. ISO/IEC 11843. Capability of detection – Part 1: Terms and definitions, Part 2: Methodology in the linear calibration case, Part 3: Methodology in the linear calibration case. January. Geneva (Switzerland): ISO. Mauricio AQ, Lins ES, Alvarenga MB. 2009. A National Residue Control Plan from the analytical perspective – the Brazilian case. Analyt Chim Acta. 637(1–2):333–336. Miller JN, Miller JC. 2005. Statistics and chemometrics for analytical chemistry. 5th ed. Edinburgh (UK): Prentice Hall, p. 131.

Oliveira EC. 2009. Comparaçaõ de Diferentes Abordagens para Avaliaçaõ da Incerteza na Cromatografia Gasosa do Ga´s Natural. Quimica Nova. 32(6):1655–1660. Silveira JN, Lara PCP, Dias MB, Matos JMG, Silva JCJ, Nascentes CC, Cimineli VS, Silva JBB. 2007. Determination of As, Bi, Cd, Co, Cr, Ga, In, Mn, Ni, Pb, Sb, Se, Sn, Te, Tl and V in antihypertensive drugs by inductively coupled plasma mass spectrometry. Atomic Spectroscop. 28:1–7. Skoog DA, Holler FJ, Nieman TA. 2002. Princı´ pios de Ana´lise Instrumental [Principles of instrumental analysis]. 5th ed. Porto Alegre: Bookman-SBQ. Ward NI, Abou-Shakra FR, Durrant SF. 1990. Trace elemental content of biological materials: a comparison of NAA and ICP-MS analysis. Biol Trace Element Res. 26–27(1):177–187. Youden WJ, Steiner EH. 1975. Statistical manual of the AOAC – Association of Official Analytical Chemists, AOAC – I. Washington (DC): AOAC, p. 35f.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 617–624

Method validation for the determination of total mercury in fish muscle by cold vapour atomic absorption spectrometry A.P. Nascimento Neto*, L.C.S. Magalha˜es Costa, A.N.S. Kikuchi, D.M.S. Furtado, M.Q. Araujo and M.C.C. Melo Laboratory of Residues and Contaminants, National Laboratory of Ministry of Agriculture, Livestock and Food Supply (LANAGRO-PA), Brazil, Av. Almirante Barroso, 1234, 66095-000, Brazil (Received 25 November 2010; final version received 8 November 2011) A method was validated for the determination of total Hg in fish muscle using continuous flow cold vapour atomic absorption (CVAAS) after microwave digestion in closed vessels. The method was validated according to European Union Regulations 333/2007 and 657/2002, considering the maximum level for the metal in fish, established by European Union regulation 1881/2006. The procedure for determining linear range, selectivity, recovery, precision, trueness, decision limit (CC ), detection capability (CC ), measurement uncertainty and robustness of the method is reported. The results of the validation process demonstrate the method fulfils the provisions of the Commission Regulation. The selectivity study indicated that there was no matrix effect on the calibration curve between the concentration range of 1.0 and 30.0 mg Hg l 1. The mean recovery calculated at six levels of fortification was in the range of 94–104%. The limit of detection (LOD) and limit of quantification (LOQ) values were 4.90 and 15.7 mg kg 1, while the CC and CC values were 0.517 and 0.533 mg kg 1, respectively, for the maximum contaminant level of 0.500 mg kg 1. The relative expanded measurement uncertainty of the method was 0.055 mg kg 1. The method was not affected by slight variations of some critical factors (ruggedness minor changes) as sample mass and volume of the HNO3 and H2O2 used in the digestion step. The method allowed accurate confirmation analyses of the CRM DORM 3Õ . In fact, the Z-scores attained in a proficiency test round were well below the reference value of 2.0, proving the excellent performance of the laboratory. Keywords: metals analysis – AAS; heavy metals – mercury; fish

Introduction Mercury (Hg) is a serious environmental toxicant andthere are several reviews on different aspects of its toxicology (Franco et al. 2007; Chan 2011; Mieiro et al. 2011). This metal accumulates in animal tissue and eventually is taken up by humans through the food chain. Fish and other seafood products usually contain significantly high concentrations of Hg. Concern over environmental pollution by Hg has intensified the search for analytical methods that require minimal sample preparation and give good analytical sensitivity (Tinggi and Craven 1996; Voergborlo and Adimado 2010). Modern Hg analysers make use of numerous technological improvements that provide more precise, accurate, and sensitive Hg determinations and less reagent consumption. These improvements include modern cold vapour atomic absorption (CVAAS) instrumentation with continuous flow and instrumentations for the decomposition of biological materials (Hight and Cheng 2005). Sample digestion using microwave heating in closed vessels is highly effective

and provides an alternative that has allowed a considerable reduction in the total time of analyses as well as the risk of contamination of the digests (Mincey et al. 1992). Besides working in a closed system, care should be taken when volatile elements are to be determined in order to match the element volatilisation with safety pressure operating conditions inside the reaction flask. This is of utmost importance for Hg determination. There is no standard digestion method for any one particular sample, but it is generally agreed that the application of high-temperature and high-pressure microwave systems drastically reduces the digestion time and cuts down on the use of high amount of acids and samples (Ombaba 1996). The validation of the analytical methods has become a basic prerequisite for those laboratories that work in the area of official food control. By applying methods validated, according to common procedures and performance criteria, the quality and comparability of the analytical results can be ensured (European Commission 2002; D’Ilio et al. 2008).

*Corresponding author. Email: antonio.nascimento@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.642009 http://www.tandfonline.com

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The aim of this study was to validate a method for the determination of total Hg in muscle fish according to Commission Decision 2002/657 and Commission Decision (EC) 333/2007 (European Commission 2002, 2007) and by considering the maximum levels established in Commission Regulation (EC) 1881/ 2006 (European Commission 2006).

Materials and methods Materials, reagents and solutions HNO3 (Sigma Aldrich, Buchs SG, Switzerland; and Merck, Darmstadt, Germany) and HCl (Carlo Erba, Rodano, Italy) of analytical grade were purified by a distillation system (Milestone, Model Duopur, Sorisole, Italy). Hydrogen peroxide (H2O2), analytical grade (Merck) was used in the digestion of samples of fish. SnCl2 2H2O (J.T. Baker, Div. Mallinkrodt, Phillipsburg, USA) was used to prepare the reducing solution. Diluent containing 7% v/v HCl was prepared by adding 140 ml HCl to approximately 1800 ml H2O in an acid-cleaned 2 L container. Reducing solution containing 10% SnCl2 2H2O and 7% HCl was prepared by mixing 35 ml HCl with approximately 300 ml H2O in a 1 L glass bottle and adding 50 g SnCl2 2H2O. The mixture was shaken to dissolve SnCl2 2H2O and diluted to 500 ml with H2O. Hg standard solutions were prepared from the 1000 mg l 1 standard (Fluka, Gillingham, UK; and Merck) by dilution in HCl 7% v/v. The calibration curve used for the analytical methodology ranged from 1.0 to 30.0 mg l 1, equivalent to 100–1500 mg kg 1 of Hg in the matrix. All the solutions were freshly prepared. The apparatus as the digestion vessels and glass materials (beakers, flasks, pipettes) was cleaned by soaking in HNO3 (purified) 10% v/v for 24 h. After the immersion in nitric acid, the vessels and glassware were thoroughly rinsed five times with Milli-Q water, dried and stored protected from contamination. Hg contamination from reagents and miscellaneous equipment was minimised by using disposable, plastic labware. Certified Reference Material The Certified Reference Material used was DORM-3 (Fish Protein Certified Reference Material for trace metals) from the National Research Council of Canada. Fish samples The fish used for the validation of the analytical methodology was acquired by the Inspection Service and Animal Health (SISA) from Para´ state. The samples were triturated and homogenised in a food

processor (Mixer B-400, Bu¨chi Labortechnik, Switzerland). Homogenised products were stored at 15 C (maximum temperature) in polypropylene flasks.

Apparatus An analytical balance (Gibertini, Crystal Model 200, Italy) was used to weigh the samples. High-purity water (resistivity 18.2 M cm) obtained by a purification system (Ultra PURELAB model ULTRAMK2, Elga Waters, USA) was used to prepare all aqueous solutions and dilutions. The microwave equipment used was a Multiwave 3000 (Anton Paar, Graz O¨sterreich, Austria). The equipment consists of a rotor HF100 with capacity for 16 digestion Teflon vessels with a capacity of approximately 100 ml. The equipment has a temperature and pressure feedback control that applies ramped microwave energy to achieve the user-selected temperature or pressure programme and which has a monitoring system that prevents pressure or temperature build up in excess of the vessel rating. A Model Quick Trace M-6100 (CETACÕ Technologies, Omaha, USA) equipped with an autosampler ASX-400, four-channel, peristaltic pump was used for Hg analysis. Liquid flow, gas flow and data acquisition parameters were entered into the software and controlled by the analyser. SnCl2 10% in HCl 7% v/v was used as the reducing agent to generate Hg vapour and it joins the sample stream at a mixing tee. The Hgþ2 in solution is reduced by Snþ2 to form Hg0 while the mixture is en route to the gas–liquid separator. The resulting finely dispersed Hg0/SnCl2 emulsion is introduced into the top of the gas–liquid separator, forming a thin film on the entire exterior surface of the frosted glass centre post. HCl/HNO3 7% v/v solution is used to clean the system along the analysis intervals and argon is the carrier gas. The carrier gas, with Hg0 vapour, passes through a drying tube where water vapour is removed and then it goes into the sample cell for the measurement of transmitted radiant power. Finally, the carrier/Hg0 gas stream is exhausted to a vapour trap where Hg0 is absorbed and clean carrier gas passes to the atmosphere (CETACÕ Technologies Inc. n.d.). The operating conditions for the technique are given in Table 1.

Methods Sample preparation Fish samples were weighed to 0.500 ( 0.0009) g in an analytical balance and transferred to the Teflon vessel. A total of 9 ml of HNO3 (purified) and 1 ml of H2O2 were sequentially added into each digestion vessel. Hg fortification solution was added as needed for

Food Additives and Contaminants Table 1. Operating condition for a concentration range 0.3–30.0 mg l 1.

Table 3. Factors and levels studied in the experimental design of two levels.

Codes

Low-level code ( 1)

High-level code (þ1)

X1 X2 X3

0.5 9 1.0

0.7 10 2.0

Condition Variables Gas flow (psig) Peristaltic pump speed (%) Sipper depth (mm) Sample uptake time (s) Rinse time (s) Read delay time (s) Replicate read time (s) Replicates Baseline correction method

40 100 145 70 90 60 2,5 4 Point 1: start read: 5 s, end read: 10 s Point 2: start read: 140 s, end read: 150 s

Table 2. Programme for sample digestion for determination of Hg in fish.

Steps 1 2 3 4

Power (W)

Temperature Ramp Time ( C) (min) (min) Ventilation

0–800 800–1000 1000–1200 1200–0

60 150 200 –

5 5 5 –

5 5 8 30

1 1 1 3

recovery experiments. Vessels were immediately assembled and placed in the microwave apparatus. Materials were decomposed using the sequence of steps established in Table 2. After the digestion step, the vessels were cooled to room temperature by placing them in a freezer for 30 min. The digested samples were transferred to Falcon tubes and diluted to a final volume of 25 ml by adding HCl 7% v/v. The samples were immediately analysed. Determination of Hg The Hg analyser was optimised by the conditions established in Table 1. The calibration curve range was 1.0–30.0 mg l 1. The concentration in fish samples (S) was calculated as: S ¼ ðC BÞ V=W where C is the concentration from the equipment reading (mg l 1); B is concentration (mg l 1) determined in the method blank; V is the volume (L) of dilution; and W (kg) is the sample mass decomposed.

Validation parameters Robustness Robustness under conditions of major changes was evaluated by using a design of experiments (DEO) according to the methodology described by Box et al.

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Sample mass (g) Volume of HNO3 (ml) Volume of H2O2 (ml)

(1978). Eight tests were performed in duplicate in a full-factorial experiment with three factors. The independents variables studied and their respective levels are given in Table 3. The experiments were carried out with spiking fish samples at 500 mg kg 1, which is the maximum level for fishery products, established by the European Union (European Commission 2006), and three factors were chosen as variables for the DEO. The response variable measured was recovery. Statistical analysis was performed using STATISTICA software version 6.0. Linearity Six calibration curves were prepared in different days at the levels of 2, 5, 10, 15, 20 and 30 mg Hg l 1 (corresponding to 100, 250, 500, 750, 1000 and 1500 mg kg 1 Hg in fish). Blanks were also prepared for each curve as a tool to adjust the zero of the equipment. Aliquots of 50, 125, 250, 375, 500 and 750 ml, respectively, of 1000 mg l 1 Hg spike solution were used and the volumes were completed to 25 ml with 7% HCl diluent. The linear model that relates analyte concentration (X) with a signal provided by the measurement instrument (Y) is: Y ¼ a0 X þ a1 where a1 is the intercept; and a0 is the slope. The ordinary least squares method (OLSM) was used to estimate a0 and a1. After an exploratory fit by OLSM, the residuals plots were examined for obvious patterns, being outliers indicated by the Grubbs test which was applied successively until no further outliers were detected. Selectivity and recovery Matrix effects and recovery were established by assays with blank and spiked samples at 100, 250, 500, 750, 1000 and 1500 mg kg 1 in six independent replicates. For selectivity evaluation, three calibration curves were prepared. The first (solvent curve) was prepared according to the procedure in linearity assessment. The second (matrix-matched curve before digestion) mercury solution was added to the matrix before the samples were digested at the microwave. The third (matrix-matched curve after digestion) used spiked

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samples at the same concentration of curve 2, but the Hg solution was added to the fish after the digestion step. Blank controls of the fish samples were analysed in the digestion batches of curves 2 and 3. The signal of spiked samples of curves 2 and 3 were compared with the absorbance of curve 1 in the same concentration. An F-test (Snedecor) and a Student’s t-test were used to evaluate the homogeneity of variance and for comparison of the means between the results. Recovery was calculated as follows: Recovery ð%Þ ¼

100 C Spiked concentration

ð1Þ

where C is the element concentration found. An acceptance limit between 80% and 120% was selected. Trueness Trueness was evaluated by Certified Reference Material (DORM-3). As stated in Commission Decision 657/2002 (European Commission 2002), the trueness of the method changes with analyte concentration; recommended values are in the range of 80– 120%. Limits and detection capability (CC ) The detection limit (LOD) and quantification limit (LOQ) were calculated from the reading of 21 fish blank samples spiked at a concentration of 1.0 mg l 1: LOD ¼ x þ 3s LOQ ¼ x þ 10s where s is the standard deviation. The critical concentration for maximum contaminant level (MCL) compliance (CC , ¼ 0.05) was calculated according to Commission Decision 657/2002 (European Commission 2002) from the MCL values of 0.50 and 1.0 mg kg 1 plus 1.64 times the standard deviation of 20 fortified samples at the MCL. CC was obtained by adding to CC 1.64 times the same standard deviation. Precision Precision was determined, based on that established in Commission Decision 657/2002 (European Commission 2002), by performing tests on three sets of blank fish samples (six replicates each) fortified with mercury at a concentration around the maximum contaminant level. The fortified levels corresponded to 0.2, 1.0 and 2.0 the maximum level (i.e., 100, 500 and 1000 mg kg 1). Samples were analysed on 3 different days with the same instrument, but by two different operators. Precision was calculated in terms of both repeatability (RSDr) and within-laboratory reproducibility (RSDR); the variability of the independent test

Table 4. Analysis of variance (ANOVA) for recovery.

Effect X1 X2 X3 X1 X2 X1 X3 X2 X3 Lack of fit Pure error Total (correlation)

Sum of squares

Degrees of freedom

Mean square

F-test

(Pr)

21.125 15.125 10.125 0.125 3.125 1.125 3.125 53.875 21.125

1 1 1 1 1 1 1 1 7

15.125 10.125 0.125 3.125 1.125 3.125 15.125 10.125 –

6.760 4.840 3.240 0.040 1.000 0.360 6.760 – –

0.234 0.272 0.323 0.874 0.500 0.656 0.233 – –

results was obtained with the same method on identical test items in the same laboratory by two operators over 3 days using the same equipment. The Horrat value, corresponding to the observed RSD divided by the RSD estimated from the Horwitz equation, was calculated in terms of both repeatability (Horratr) and within-laboratory reproducibility (HorratR).

Measurement uncertainty In this study, the following contributions to combined measurement uncertainty were selected: preparations of the standard solution, sample mass, standard uncertainty associated with the recovery, uncertainty associated with the calibration curve, and withinlaboratory reproducibility of the measurements. Each component was calculated according to the International Organization for Standardization (ISO) (1995).

Results and discussion Robustness To estimate the effect of the independent variables in the response, recovery (R) was performed by an analysis of variance (ANOVA), as indicated in Table 4. Table 4 shows the effects of factors on the response with the linear (main effect) and cross-terms (interaction effect), which can be evaluated based on the statistics F and Pr. The results in Table 4 show that the isolated variables X1 (mass of sample), X2 (acid volume), X3 (peroxide volume) and combinations of all variables were not statistically significant to the response R at a significance level of 0.05, which can be confirmed in Figure 1. This result demonstrates that the variations in the sample mass and volume of nitric acid or hydrogen peroxide had no effect on the results of the method in the ranges studied.

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Figure 1. Individual and combined effects for the response.

Linearity of calibration curve The calibration curve, obtained by plotting the peak area of the six series of analyses, expressed in absorbance units versus concentration in the range 2.0–30 mg l 1, gave the linear regression equation y ¼ 7353.1x þ 2554.8 with a determination coefficient (r2) of 0.999. The OLSM was used for the treatment of the results. No outliers were detected by Grubbs tests.

Table 5. Comparison of determined and certified concentration for Hg in the reference material. Certified Reference Material NRC-CNRC DORM-3

Certificate value (mg kg 1)

Determined concentration (mg kg 1)

Trueness (%)

0.382 0.060

0.345 0.001

91.0

Selectivity The data obtained for the replicates of each level were compared using an F-test (Snedecor) to evaluate the homogeneity of variances. The means between the results obtained were compared using the Student’s t-test. The analysis showed no difference between the values obtained from standard solutions and spiked blank samples. This observation confirms that there is no matrix interfering and that microwave digestion could provide a complete digestion of the investigated samples. This result is satisfactory with respect to acceptance criteria established in Commission Decision 333/2007 (European Commission 2007), where specificity must be free from matrix or spectral interferences. Based on these results for validation and routine analysis, calibration curves in solution were used.

Limit of detection (LOD) and limit of quantification (LOQ) LOD and LOQ values were, respectively, 4.9 and 15.7 mg kg 1 in matrix. For Hg, Commission Decision 333/2007 (European Commission 2007) has fixed a maximum value for LOD of 50 mg kg 1 and LOQ of

100 mg kg 1; the present method achieved a value well below this limit (European Commission 2006).

Trueness Trueness was established by assays with CRM DORM 3Õ in seven independent replicates. The samples were split into different batches and solvent curves were prepared to calculate the analyte concentration in the spiked samples. The mean and standard deviation were calculated. The result is shown in Table 5. According the criteria stated in European Commission Decision 657/2002 (European Commisssion 2002) for mass fractions, the minimum trueness ranged from 80% to 110%. Based on the acceptability criteria, no lack of trueness was observed.

Precision As presented in Table 6, both RSDR and RSDr values were lower than those calculated by the Horowitz equation. This result indicates that the method satisfies minimum performance criteria established by

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Commission Decision 333/2007 (European Commission 2007). The result also indicates Horrat values lower than 2.

Recovery The media of recovery results for each spiked level are presented in Table 7, with the respective coefficient of variation (CV). Table 6. Repeatability and within-laboratory reproducibility for the determination of Hg in spiked fish samples. Fortification level (mg kg 1) 100 500 1000

Parameter

Op 1

Op 2

Overalla

RSD (%) Horrat value RSD (%) Horrat value RSD (%) Horrat value

12.70 0.56 4.29 0.24 2.38 0.15

14.12 0.62 7.05 0.40 3.85 0.24

13.27 0.59 5.83 0.33 3.16 0.20

Notes: Op, operator (relative standard deviation under repeatability conditions (RSDr). a Relative standard deviation under within-laboratory reproducibility conditions (RSDR). SD, standard deviation.

The results for the recovery tests were particularly successful being in the acceptance range of 80–120%. For all levels analysed, the CV% did not exceed the limit of 25%. The individual values obtained in the recovery study are presented in Figure 2. Hight and Cheng (2005) developed a method for total Hg determination using 5 ml of HNO3 and 1 ml of 1% w/v NaCl in the digestion step. NaCl was added to prevent Hg loss. The authors evaluated the use of H2O2 in the decomposition of samples and concluded that H2O2 addition after microwave digestion produced poorly shaped Hg signals. In the present method, the good recovery obtained from spiked samples showed that the use of H2O2/HNO3 at higher temperatures gives complete oxidation of organic matter. The addition of NaCl was discontinued because no Hg loss was observed. The signals produced by the Hg analyser were reproducible and the precision of replicates readings was less than 1%.

Decision limit (CCa) and detection capability (CCb) CC and CC values were 0.517 and 0.533 mg kg 1, respectively, for the MCL of 0.500 mg kg 1. Considering the 1.0 mg kg 1 MCL, the values were 1.020 and 1.040 mg kg 1.

Expanded measurement uncertainty Table 7. Media of recovery results. 1

Level (mg kg ) 100 250 500 750 1000 1500

Recovery (%)

CV (%)

94.33 101.97 105.70 104.58 102.68 103.12

3.85 1.43 1.43 3.09 1.21 0.69

Figure 2. Recovery obtained for Hg.

According to Commission Decision 333/2007 (European Commission 2007), appropriate methods for official control must produce results with standard measurement uncertainties below the maximum standard measurement uncertainty calculated by Equation (2): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi LoD 2 Uf ¼ þð CÞ2 ð2Þ 2

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Figure 3. Contribution of each factor to the overall combined uncertainty for Hg.

where Uf is the maximum standard uncertainty of measurement (mg kg 1); LOD is the limit of detection (0.489 mg kg 1); C is the concentration of interest (mg kg 1), which corresponds to the maximum contaminant level (500 mg kg 1); and is the numerical factor whose value depends on the use of C. In the case of the maximum contaminant established for the analyte in question, ¼ 0.18. The expanded measurement uncertainty at the contamination level of 0.500 mg kg 1 was calculated by using a coverage factor of 2, corresponding approximately to the 95% confidence level. The uncertainty values with respect to MCL for total Hg were 0.055 mg kg 1. According to Figure 3 the major contributions were found to be the reproducibility standard deviation and the calibration function; the others contributions due to recovery, volume and mass were negligible. The value is lower than the maximum standard uncertainties requested by Commission Decision 337/2007 (European Commission 2007).

determination of total Hg in fish was validated according to Commission Decision 657/2002 (European Commission 2002) and Regulation 2007/ 333/EC, in terms of selectivity, recovery, precision, trueness, decision limit, detection capability and ruggedness. The result of the validation process demonstrated the agreement of method performances with the provisions of Commission Decision 333/2007 (European Commission 2007). The expanded measurement uncertainty value, obtained by validation data and excellent results achieved in a proficiency test round, confirmed the laboratory technical competence in the determination of total Hg in fish samples, with the accuracy level corresponding to the requirements of recent European regulation.

Acknowledgements The authors thank CNPQ for a scholarship support programme; and the National Agriculture Laboratory at Para´ State/MAPA for funds and facilities.

Proficiency test round In December 2009 the method was submitted to a proficiency test round organised by FAPAS/UK for 103 participants. Canned fish sample with an unknown concentration of Hg was analysed in duplicate and the results for total Hg were evaluated in terms of Z-score. The Z-score obtained in the test performance was 0.5, well below the reference value of 2.0. Together with the use of validated methods, proficiency testing is an essential element of laboratory quality assurance.

Conclusions An optimised analytical method based on microwave digestion followed by CVAAS detection used for the

References Box GEP, Hunter WG, Hunter JS. 1978. Statistics for experiments: an introduction to design, data analysis and model building. New York (NY): Wiley. CETACÕ Technologies Inc. n.d. Determination of mercury in whole blood using the CETACÕ M-6000A automated mercury analyzer. Omaha (NE): CETACÕ Technologies Inc. Chan HM. 2011. Mercury in fish: human health risk. In Encyclopedia Environ Health: Michigan; p. 697–704. D’Ilio S, Petriccu F, D’Amato M, Di Gregorio M, Senofonte O, Violante N. 2008. Method validation of arsenic, cadmium, chromium and lead in milk by means of dynamic reaction cell inductively coupled plasma mass spectrometry. Analytica Chimica Acta. 62:59–67.

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European Commission. 2002. Commission Decision No. 657/2002 of 12 August (2002) implementing Council Directive 96/23/EC concerning the performance of analytical methods and interpretation of results. Off J Eur Comm. L221:8–36. European Commission. 2006. Commission Decision No. 1881/2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs. Off J Eur Comm. L364:5–24. European Commission. 2007. Commission Decision No. 333/2007 of 28 March 2007 laying down methods of sampling and analysis for the official control of levels of lead, cadmium, mercury, inorganic tin, 3-MCPD and benzo (a) pyrene in foodstuffs. Off J Eur Comm. L88:29–38. Franco J, Braga H, Nunes A, Ribas C, Stringari J, Silva A, Pomblum S, Moro A, Bohrer D, Santos A, et al. 2007. Lactational exposure to inorganic mercury: evidence of neurotoxic effects. NeurotoxicolTeratol. 29:360–367. Hight SC, Cheng J. 2005. Determination of total mercury in seafood by cold vapor-atomic absorption spectroscopy (CVAAS) after microwave decomposition. Food Chem. 91:557–570.

International Organization for Standardization (ISO). 1995. ISO/IEC Guide 98. Guide to the expression of uncertainty in measurement (GUM). Geneva (Switzerland): ISO. Mieiro CL, Pereira ME, Duarte AC, Pacheco M. 2011. Brain as a critical target of mercury in environmentally exposed fish (Dicentrarchus labrax) – Bioaccumulation and oxidative stress profiles. Aquatic Toxicol. 103:233–240. Mincey D, Williams RC, Giglio JJ, Graves GA, Pacella AJ. 1992. Temperature controlled microwave oven digestion system. Analytica Chimica Acta. 264:97–100. Ombaba JM. 1996. Total mercury determination in biological and environmental standard samples by gold amalgamation followed by cold vapor atomic absorption spectrometry. Microchem J. 53:195–200. Tinggi U, Craven G. 1996. Determination of total mercury in biological materials by cold vapor atomic absorption spectrometry after microwave digestion. Microchem J. 54:168–173. Voergborlo RB, Adimado AA. 2010. A simple classical wet digestion technique for the determination of total mercury in fish tissue by cold-vapor atomic absorption spectrometry in a low technology environment. Food Chem. 123:936–940.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 625–632

Method development for the control determination of mercury in seafood by solid-sampling thermal decomposition amalgamation atomic absorption spectrometry (TDA AAS) D.P. Torresab, M.B. Martins-Teixeiraa, E.F. Silvaa and H.M. Queiroza* a

Laborato´rio Nacional Agropecua´rio em Sa˜o Paulo – Lanagro-SP, Ministe´rio da Agricultura, Pecua´ria de Abastecimento – MAPA, 13100-105, Campinas, SP, Brazil; bInstituto de Quı´mica – Universidade Estadual de Campinas – Unicamp, 13083-897, Campinas, SP, Brazil (Received 25 November 2010; final version received 7 November 2011) A very simple and rapid method for the determination of total mercury in fish samples using the Direct Mercury Analyser DMA-80 was developed. In this system, a previously weighted portion of fresh fish is combusted and the released mercury is selectively trapped in a gold amalgamator. Upon heating, mercury is desorbed from the amalgamator, an atomic absorption measurement is performed and the mercury concentration is calculated. Some experimental parameters have been studied and optimised. In this study the sample mass was about 100.0 mg. The relative standard deviation was lower than 8.0% for all measurements of solid samples. Two calibration curves against aqueous standard solutions were prepared through the low linear range from 2.5 to 20.0 ng of Hg, and the high linear range from 25.0 to 200.0 ng of Hg, for which a correlation coefficient better than 0.997 was achieved, as well as a normal distribution of the residuals. Mercury reference solutions were prepared in 5.0% v/v nitric acid medium. Lyophilised fish tissues were also analysed; however, the additional procedure had no advantage over the direct analysis of the fresh fish, and additionally increased the total analytical process time. A fish tissue reference material, IAEA-407, was analysed and the mercury concentration was in agreement with the certified value, according to the t-test at a 95% confidence level. The limit of quantification (LOQ), based on a mercury-free sample, was 3.0 mg kg 1. This LOQ is in accordance with performance criteria required by the Commission Regulation No. 333/2007. Simplicity and high efficiency, without the need for any sample preparation procedure, are some of the qualities of the proposed method. Keywords: statistical analysis; metal determination – AAS; method validation; quality assurance; environmental contaminants; heavy metals – mercury; trace elements (toxic); fish and fish products; seafood

Introduction The consumption of fish has several advantages for human health, such as the high content of omega 3. However, piscivorous species may bioaccumulate organic and inorganic contaminants, which enter the food chain. Mercury (Hg) toxicity is well known because of its accumulative and persistent character in the environment; and fish is one of the major sources of human dietary exposure. Organic Hg species, highly noxious and fat soluble, have a tendency to accumulate in fish tissue, from where they can enter the human food chain (Flores et al. 2001; Krishna et al. 2005; Torres et al. 2005; Vieira et al. 2007). Because of their highly toxic nature, there is a growing demand for developing fast and sensitive procedures for the determination of toxic species in biological materials, particularly foodstuffs. For Hg determination in such samples, cold vapour atomic absorption spectrometry (CV AAS) is the most common method, due to its high sensitivity and easy operation (Tao et al. 1998, 1999;

Ribeiro et al. 2004; Rodrigues et al. 2009; Torres, Borges, et al. 2009, Torres, Frescura, et al. 2009). The decomposition of organic and inorganic matrices is a critical stage in trace metal determination, since it largely determines the precision and accuracy of the results. A wide variety of combinations of strong acids, oxidants, UV radiation, and elevated temperatures and pressures has been used and recommended (Willie et al. 1997). With the intention to develop a new method for the simple, fast and reliable Hg determination in fresh fish samples, the Direct Mercury Analyzer DMA-80 (Milestone, Sorisole, Italy) can be employed for the direct analysis of liquid and solid samples. This system operates on a basis of sample thermal decomposition, Hg catalytic reduction, a gold amalgamation system for vapour Hg trapping, Hg desorption and atomic absorption spectrometry, which can be called thermal decomposition/amalgamation atomic absorption spectrometry. The DMA-80 instrument does not require any pre-treatment of the samples, apart from

*Corresponding author. Email: helena.queiroz@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.642310 http://www.tandfonline.com

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homogenisation; it is equally suitable for the analysis of solid and liquid materials, and has a limit of detection (LOD) at the mg kg 1 level (US Environmental Protection Agency (USEPA) 2007). Such qualities have meant it is frequently employed for Hg determination in various matrices (Houserova et al. 2005; Rahman and Kingston 2005; Haynes et al. 2006; McClain et al. 2006; Ikem and Egilla 2008; Carbonell et al. 2009; Maggi et al. 2009). The aim of this work is to develop a simple, rapid and cost-effective method for the determination of total Hg in fish samples by solid sampling thermal decomposition/amalgamation atomic absorption spectrometry (SS-TDA AAS) to control this inorganic contaminant concentration in Brazilian fresh fish. To the best of our knowledge, a full study of TDA AAS parameters is still lacking and it is therefore useful to check its viability, which, in the authorsâ&#x20AC;&#x2122; opinion, is fundamental for the better use of the equipment.

Materials and methods Instrumentation Total Hg determination was performed using the Direct Mercury Analyzer (DMA-80). A gold amalgamator system (Milestone, DMA 8134) was used for Hg vapour trapping; and a catalyser system (Milestone, DMA 8333) was employed for Hg reduction. Quartz sample boats (Milestone, DMA 8347) were used for sample weighting and analysis in all measurements, except for particular measurements employing nickel sample boats in robustness experiments. The drying and pyrolysis temperatures and periods employed were 200 C for 90 s and 650 C for 120 s, respectively. The amalgamator selectively traps Hg after the system is flushed with oxygen to remove any remaining gases or decomposition products. The amalgamator is then heated, releasing the Hg vapour. The absorption intensities were measured at 253.7 nm, using peak height for signal processing. Oxygen (White Martins, SaË&#x153;o Paulo, Brazil) was used as reagent and carrier gas for Hg vapours.

Reagents and materials The following reagents were used: 65% nitric acid (Merck, Darmstadt, Germany), which was distillated in a quartz sub-boiling apparatus DuoPUR (Milestone). The water (resistivity of 18.2 M cm) was deionised in a Milli-Q system Integral 5 (Millipore, Bedford, MA, USA). Inorganic Hg reference solutions were prepared daily by sequential dilution of a 1000 mg l 1 inorganic Hg stock solution (Fluka, Buchs, Switzerland). The certified reference material analysed was fish tissue IAEA-407 (Analytical Quality Control Services, Vienna, Austria).

Sample preparation procedure Fish samples were prepared based on simple muscle homogenisation using a domestic meat mixer model HC31 (Black & Decker, Uberaba, MG, Brazil), after removing skin, bone and viscera with the aid of a stainless steel knife (30 cm, Mundial, Sao Paulo, SP, Brazil). The muscle was cut off in small cubes before being processed by the mixer. One naturally contaminated fish sample LG (Lophius gastrophysus) and two fish samples with a low Hg concentration ON (Oreochromis niloticus) and SP (Sardina pilchardus), purchased from a supermarket in Campinas city, were employed for method optimisation. Samples were homogenised mostly using the domestic mixer, whilst some measurements were conducted with the same samples after careful homogenisation with a stainless steel knife. A mass of about 100.0 mg of homogenised muscle was loaded in quartz boats and inserted into the DMA-80 system in order to determine total Hg. Unless otherwise specified, all measurements refer to wet weight. Samples ON and SP were submitted to an alternative preparation procedure: muscle lyophilisation after mixer homogenisation. A lyophiliser LS3000 (Terroni, Sao Carlos, SP, Brazil) was employed in this step.

Optimisation of the Hg quantification in fresh fish Hg determination in the samples by using the DMA-80 was achieved as follows. Firstly, an aliquot of previously weighed sample was dried and subsequently thermally and chemically decomposed in an oxygenated decomposition furnace. The remaining decomposition products were then carried to an amalgamator that selectively traps Hg. Flowing oxygen carries the Hg vapour through absorbance cells. The system has two absorbance cells, which are in a rate of 10:1. Peak height was used for signal evaluation. The DMA-80 employment for solid-sample analyses provides advantages for the analytical process, since it is not necessary to adopt a sample digestion procedure. A simple homogenisation of the sample is enough to perform the analysis. The results for the lyophilisation have not showed any advantage; instead, the total analytical process time increased considerably and poorer precision was obtained compared with the direct fresh fish analysis. Additionally, cross-contamination occurred for the ON sample, which had a very low Hg concentration. Therefore, fresh fish analysis was the adopted condition for further experiments. The samples were then easily and quickly analysed. They did not require aggressive chemical pre-treatment which contribute to decrease the susceptibility to contamination and analyte loss. The equipment allows aqueous standards to be used in the calibration process (USEPA 2007). Some additional experimental

Food Additives and Contaminants parameters were evaluated: linear range of the calibration curves, Hg pre-concentration in the amalgamator system, memory effect of the system and method robustness. Statistical software The statistical software STATISTICA 7.0 (StatSoft Inc., Tulsa, OK, USA) was used for the factorial analysis optimisation of seven experimental factors (robustness) through the Experimental Design tool.

Results and discussion Method robustness The robustness for the determination of Hg in fresh fish samples by TDA AAS was evaluated by employing

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the Youden test, according to Commission Decision No. 657/2002 (European Commission 2002). This test allows the evaluation of not only method robustness, but also the influence of each of the variations in the final results. Thus, Table 1 was filled in with a nominal value for the seven factors, described by the current method, and its variations, for method robustness evaluation. Nominal values are represented in capitals letters and their variations in small letters. These factors must be arranged to assess robustness. To determine the variation of one factor, it is necessary to find the result for the four values corresponding to the capitals letters and to subtract the resulting mean value from the four values corresponding to the small letters. Figure 1 shows the normal graph for the effects values referent to the experimented factors in the method robustness investigation.

Table 1. Nominal values for the seven factors, described by the present method, and their variations for method robustness evaluation and effect estimates for the 27 4 III design. Mean square pure error ¼ 848.3935. Factor Sample mass (mg) Sample preparation Drying time (s) Combustion time (s) Container Stand for reading (min) Fish species Mean interaction

Nominal

Variation

Effecta

t(8)

95% Confidence limit

þ95% Confidence limit

100.0 (A) Mixer (B) 90 (C) 120 (D) Quartz (E) 0 (F) ON (G) –

50.0 (a) Knife (b) 56 (c) 140 (d) Nickel (e) 30 (f) SP (g) –

46.2501 14.7096 16.6636 12.9767 5.6993 14.4620 8.3502 490.2931

3.17573 1.01002 1.14420 0.89104 0.39134 0.99302 0.57336 67.33131

12.6664 48.2933 16.9201 20.6070 39.2830 48.0457 25.2335 473.5012

79.8338 18.8742 50.2473 46.5605 27.8845 19.1217 41.9339 507.0849

Note: aObtained from measurements performed by the addition of 500.0 mg kg 1 of Hg, the maximum mercury level for farm fish, established by EC Regulation No. 1881/2006 (European Commission 2006).

Figure 1. Normal graph for the effects values referent to the experimented factors in the method robustness investigation. It seems that just effect (1)A is significant.

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D.P. Torres et al. Table 2. Figures of merit for calibration ranges from DMA-80. Curvea Cell Cell Cell Cell

1 1 2 2

(Linear) (Square) (Linear) (Square)

Regression equation

Mass range (ng of Hg)

0.9965 0.9980 0.9978 0.9982

A ¼ 0.0486Hg 0.0027 A ¼ 0.0004Hg2 þ 0.0571Hg 0.0369 A ¼ 0.0009Hg þ 0.0011 A ¼ 4 10 7Hg2 þ 0.0008Hg þ 0.0043

2.5–20.0 2.5–20.0 25.0–200.0 25.0–200.0

Note: aCurves were obtained by sequential reading and pipetting different volumes from two standard solutions: 100 mg l 1 for low range and 500 mg l 1 for high range.

The points of the normal graph adjust very well to a straight line, which crosses the normal value equal to zero (or cumulated probability of 50%) over the zero point of the horizontal axis. It makes sense, therefore, to consider these points as coming from a normal population of mean equal zero, i.e. they represent insignificant effects. It is not possible to say the same about factor A (sample mass). This point, so far from the other points, would hardly belong to the same population, which produced the central points because the experiments were performed randomly. This conclusion was confirmed by the effects calculation using the mentioned statistical software. The standardised effect estimate or absolute value (Student’s t-value) for the sample mass factor was t(8) ¼ 3.176, which was the only one higher than those for eight degrees of freedom at a 95% confidence level on the Student’s t-table, namely 2.306. The standardised effect estimate, t(8) in Table 1 was obtained from the ratio of the effect value for each factor and the pure (standard) error (Bruns et al. 2006). In this context, sample mass variation was the most important investigated factor and there is an experimental restriction for its variation. Additionally, at a 95% confidence level, the interval for the sample mass factor was [12.6664; 79.8338], the only one in which the zero was not contained. Thus, the null hypothesis was rejected, i.e. the existence of the significance of factor A was proved. Such a finding clearly shows the restriction for the use of solid certified reference materials for DMA-80 calibration, once in this case sample weights significantly different from 100 mg might be employed. Taking these facts into consideration, the sample mass was fixed at 100 5 mg.

Figures of merit At least two calibration curves are needed to work with DMA-80, provided that there are two absorbance cells. The first working range, related to the absorbance of cell 1, frequently includes responses from 0.01 to 20 ng of Hg (low range). The second, related to the absorbance of cell 2, includes responses from about 20 to 1000 ng of Hg (high range). An ultratrace calibration range is also possible at the beginning of cell 1. The

calibration was completed for the low and high ranges, although the final Hg amount of 200 ng was not exceeded in any of the studies, the aim being to minimise the memory effect of the system, which used to be strong for high Hg concentrations. This topic will be discussed below. The figures of merit obtained for this investigation are presented on Table 2, which were obtained by reading Hg concentrations sequentially. As shown in Table 2, the absorbance values for the whole range of Hg content (cells 1 and 2) seem to fit very well in all regressions, as judged by correlation coefficient values. However, during the analysis of residuals to check the homoskedasticity, independence and normality of the data, it was found that although conformity was demonstrated for the high calibration range, the behaviour for the low calibration range was slightly different. In this case, it was necessary to include a square component in the regression equation in order to solve the issue of dependent and abnormal residuals. These discrepancies, however, could not be detected solely by the analysis of the curve correlation coefficient, which would lead to an incorrect judgment. A possible explanation for the need to include the square component in the low range regression, which is uncommon for AAS purposes, is that for this technique calibration curves are only linear for absorbance values up to about 0.5–0.8 (Welz and Sperling 1999), and the absorbance values for the low range achieve Hg contents of about 10 ng, basically half the range. Whereas the calibration range of cell 2 was just scanned for Hg amounts up to 200 ng, the square component could not be justified and the fit for this model with a square component would complicate the calculation of the regression and require more information. A pool of investigations on the linearity for AAS is shown by Welz and Sperling (1999), who mention that in AAS, where the standard deviations for all levels of calibration exhibit very similar values, the calibration function can often be described by a simple linear regression. More complicated procedures for calculation such as weighted regression can improve the accuracy, but they require more information so that the effort needed for calibration is markedly higher. Additionally, including the square component in the high calibration range is not a condition recommended by the authors once this is an

Food Additives and Contaminants

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Table 3. Mass (ng of Hg) and mercury concentration (mg l 1), relative standard deviation (RSD) and obtained recovery for the evaluation of the pre-concentration tool of the equipment (n ¼ 2). Pre-concentrationa No Yes (once)

Mass found (ng of Hg)

Concentration (mg l 1)

RSD (%)

Recovery (%)

19.87 39.68

496.79 496.05

1.11 3.58

99.36 99.21

Note: a20.0 ng of mercury.

unsafe condition, which can force the data to seem better than they really are. A point to note is that the term that goes with ‘Hg’ is very similar for both regressions: 0.0009 and 0.0008, respectively. This fact indicates that the linear regression may be considered more appropriate in this mass range since this model is more easily explained. According to these results, the following was defined: for Hg masses up to 20.0 ng, the square regression is employed (cell 1); for Hg masses from 25.0 ng the linear regression was chosen (cell 2). Because of the large calibration range of DMA-80, its calibration is quite laborious, demanding a whole day or more. On the other hand, the obtained curves can be used for months after a daily check. If the correction factor for the curve is between 0.9 and 1.1, such a curve can be used without any concern (USEPA 2007).

Limit of detection (LOD) and limit of quantification (LOQ) The LOD is the smallest measured content from which it is possible to deduce the presence of the analyte with a statistical certainty of 95%. It is numerically equal to three times the standard deviation of the measurements on a blank sample (n 4 20). On the same hand, the limit of quantification (LOQ) is the lowest content of the analyte from which it is possible to determine it with a 95% confidence level. If accuracy and precision are constant in a range of concentrations around the LOD, so the LOQ is equal to six or 10 times the standard deviation of tests on a blank sample (n 4 20), according to the requirement established by Regulation No. 333/2007 (European Commission 2007). To establish the limit of detection of the method (LDM) and the LOQ, 21 measurements of the sample low in Hg ON were made. The calculated LDM was 0.78 mg kg 1 (1.0 mg kg 1); the calculated LOQ was 2.59 mg kg 1 (3.0 mg kg 1). This value is in accordance with the requirement established by EC Regulation No. 333/2007, which demands a LOQ lower than 20% of the maximum level of the contaminant (ML) for Hg (500 and 1000 mg kg 1 for farming and capture fish, respectively). To make it possible to

set up the LDM and LOQ values, it was necessary to construct an ultratrace calibration curve, since the Hg concentration values measured in this situation are quite low. For this curve, from 1.0 to 5.0 ng of Hg, an R2 equal to 0.9993 and a regression equation A ¼ 0.0502 Hg þ 0.0023 were obtained. The residuals were homoscedastic, independent and normal. After that, a confirmation procedure of the obtained value for the LOQ was carried out. In order to confirm the value of the LOQ, the following procedure was adopted: the addition of Hg solution was realised directly over the sample mass to be measured. In this way, 50.0 ml of a 10.0 mg l 1 Hg standard solution was added into the sample aliquot of about 100 mg, resulting a concentration of 5.0 mg kg 1, which was immediately weighted, spiked and analysed. This procedure was repeated 15 times and the recovery value found was 93.5% at the LOQ, which is in agreement with the requirement of validation rules followed in this work, in Commission Regulation (EC) No. 333/2007 and in Commission Decision 2002/657/EC.

Mercury pre-concentration in the amalgamator system The Hg vapour pre-concentration system was evaluated from the measurement of 40.0 ml of a 500.0 mg l 1 Hg standard solution, which means 20.0 ng of Hg, under the employed condition. The results, shown in Table 3, demonstrate that one pre-concentration step of 20.0 ng of Hg, which results in 40.0 ng of Hg, supplies the same concentration value than that of the direct measurement of 20.0 ng of Hg. For the measurement of 20.0 ng of Hg, the calibration curve used was with square regression up to 20.0 ng (cell 1), and for the measurement with the pre-concentration step it used the calibration curve with linear regression of cell 2. Such findings give the DMA-80 users the flexibility to apply the square regression for cell 1, when Hg concentration falls in the range beyond 20.0 ng (the gap between cell 1 and cell 2), or the pre-concentration tool, since the result obtained with the square curve for cell 1 is the same as that obtained with one

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D.P. Torres et al. Table 4. Mercury concentrations on dry mass base, relative standard deviation and recovery obtained for samples analysed by TDA AAS. Obtained values (mean confidence limit) are in mg kg 1, n ¼ 2.

Sample LG LGa ON ONa SP SPa IAEA-407

Certified (mg kg 1)

Found (mg kg 1)

RSD (%)

Recovery (%)

n.a. n.a. n.a. n.a. n.a. n.a. 222 6

111.54 12.07 207.19 9.96 53.00 b 103.16 0.63 29.67 0.56 131.92 4.42 225 7

4.4 1.9 n.a. 0.2 1.9 3.4 1.3

n.a. 95.6 n.a. 103.2 n.a. 102.2 101.4

Notes: aAddition of 10.0 ng of Hg (100.0 mg kg 1) at the measurement moment over the sample aliquot (about 100 mg). b Limit of quantification; n.a., not applicable.

pre-concentration step employing the linear regression for curve from cell 2.

Memory effect The memory effect was investigated because, after measuring high Hg contents, relatively high absorbance values were recorded for the measurement of the system without any solution or sample. In this sense, after reading eight standard solutions which resulted in Hg masses between 100.0 and 400.0 ng, it was possible to observe that the absorbance values recorded for 11 sequential measurements of the system without any solution or sample were quite high, being the two first absorbance values obtained right after the measurement of 400.0 ng of Hg equal to an Hg mass of 7.5 ng, which means 75 mg kg 1, under the employed conditions (100 mg of sample or 100 ml of standard solution). Even after 11 measurements of the ‘empty’ system, the absorbance value still achieves an Hg mass of about 1.0 ng (10 mg kg 1). This investigation showed the importance of the remnant Hg collection from the system before starting the measurement of a subsequent sample or solution, mainly if it bears a lower Hg concentration than the preceding one, which could result in a systematic error. Recently, we found that the strong memory effect was a consequence of the old batch of the employed catalyst. As would be expected, the memory effect is stronger with the ageing of the catalyst and after measuring samples with a high Hg concentration. After that, it was common of the absorbance values for the ‘empty’ system to reach 10–20% of the Hg content of the previously analysed sample. With catalyst ageing, another difficulty is reaching the daily check parameter of the calibration curves. Thus, calibration frequency is higher. After changing the catalyst, the absorbance value indicated by the manufacturer is 0.0030 (for the cell 1). Running blanks every 10 measurements is good

practice and recommended by the USEPA (2007). Furthermore, running blanks daily before starting work is advisable as part of the routine.

Analytical application The results for three real samples analysed and the certified sample IAEA-407 are shown in Table 4. The recovery test was applied to ON and LG samples and values ranged between 95.6% and 103.2% were obtained. On the same hand, the determined value for total Hg in the reference material analysed agrees with the certified content within a 95% confidence level, demonstrating the accuracy of the developed method. After accomplishing validation, fulfilling international requirements – Commission Regulation (EC) N 1881/2006 (European Commission 2006), Commission Regulation (EC) No. 333/2007 (European Commission 2007) and DOQ-CGCRE-008 – the developed method was implemented for routine use in the Laborato´rio Nacional Agropecua´rio em Sa˜o Paulo (Lanagro-SP) for Hg control determination in Brazilian fish. Indeed, real unknown samples from nationwide monitoring programmes are being safely analysed for Hg content using this robust and straightforward method with an extensive concentration range.

Considerations about DMA-80 A low-Hg content sample must not follow the analysis of a relatively high-Hg content sample. This would force the reanalysis. In our software version, Rev 02-B, being different to that declared by Butala et al. (2006), samples are analysed following the order in which they are listed, apart from the sample mass insertion order. Another event that should be noted is the stability of Hg from a standard solution after dispensing it on a quartz sample boat positioned at the autosampler. Even using 5.0% v/v nitric acid for Hg stabilisation,

Food Additives and Contaminants it was not possible to measure it anymore after about 30 min owing to analyte loss. That was not observed for sample analysis. As observed by Butala et al. (2006) and in our robustness study, there is no statistical difference in sample analysis by using quartz or nickel boats. However, when reading standard solutions in an acidic medium, lower absorbance values were obtained for quartz boats. Therefore, the use of the quartz boats was fully adopted once its lifetime is rather higher in comparison with the nickel ones, which was judged to be more valuable. The use of quartz boats is also a recommendation from the manufacturer. Another conclusion from the robustness study is discouraging using sample masses lower than 100 mg when dealing with high-Hg concentration samples. Diluting sample could be a better alternative. Despite being an option of the software, two things are not advisable when dealing with DMA-80 calibration regression. First, the calibration curve should never force the intercept through the origin; and second, for Hg contents up to 200–400 ng, linear regression must be preferable.

Conclusions The method development for Hg determination in fresh fish samples using DMA-80 was successful. The total analytical process is fast, environmentally friendly, cost-effective and does not require any complex sample preparation, which minimises risk of sample contamination. Some points have to be highlighted when using DMA-80, such as linearity evaluation and sensitivity over time, memory effect of the amalgamation system and robustness, which was not discussed by any of the DMA-80 users cited in this work, except USEPA (2007) which clearly mentions memory effect. This simple method can be useful for the routine analysis of a variety of fresh fish, as no noticeable influence of the matrix on the characteristics of the method could be noticed so far. The calibration curves from 2.5 to 20.0 ng of Hg and beyond 25.0 ng of Hg had satisfactory linearity; however, only for the first cited range of Hg masses is the square regression advisable. The LOQ proved to be adequate for control investigation according to European Union regulations. Additionally, the robustness study, which has not been reported elsewhere, showed that sample mass is a factor to be controlled for successful Hg determination on DMA-80. In conclusion, the method was shown to be accurate and robust.

Acknowledgements The authors are thankful to the Ministe´rio da Agricultura, Pecua´ria e Abastecimento (MAPA) for financial support,

631

and to the Conselho Nacional de Pesquisa e Desenvolvimento Tecnolo´gico (CNPq, Brazil) for a scholarship (D. P. Torres). The invaluable contributions of Angelo de Queiroz Mauricio, Maria de Fa´tima Martins Pinhel, Carolina Costa Mota Paraiba and Roy Edward Bruns are acknowledged.

References Bruns RE, Scarminio IS, de Barros Neto B. 2006. Statistical design – chemometrics. Amsterdam (The Netherlands): Elsevier. Butala SJM, Scanlan LP, Chaudhuri SN. 2006. A detailed study of thermal decomposition, amalgamation/atomic absorption spectrophotometry methodology for the quantitative analysis of mercury in fish and hair. J Food Prot. 69:2720–2728. Carbonell G, Bravo JC, Fernańdez C, Tarazona JV. 2009. A new method for total mercury and methyl mercury analysis in muscle of seawater fish. Bull Environ Contam Toxicol. 83:210–213. DOQ-CGCRE-008 – Orientaçaõ sobre validaçaõ de me´todos de ensaios quı´ micos – Revisaõ 02 Junho 2007. Rio de Janeiro, Brazil: INMETRO. European Commission. 2002. Commission Decision of 12 August 2002 implementing Council Directive 96/23/ EC concerning the performance of analytical methods and the interpretation of results – 2002/657/EC. Brussels, Belgium: European Commission. European Commission. 2006. Commission Regulation (EC) N 1881/2006, Setting maximum levels for certain contaminants in foodstuffs. Brussels, Belgium: European Commission. European Commission. 2007. Commission Regulation (EC) N 333/2007, Laying down the methods of sampling and analysis for the official control of the levels of lead, cadmium, mercury, inorganic tin, 3-MCPD and benzo(a)pyrene in foodstuffs. Brussels, Belgium: European Commission. Flores EMM, Welz B, Curtius AJ. 2001. Determination of mercury in mineral coal using cold vapor generation directly from slurries, trapping in a graphite tube, and electrothermal atomization. Spectrochim Acta B. 56:1605–1614. Haynes S, Gragg RD, Johnson E, Robinson L, Orazio CE. 2006. An evaluation of a reagentless method for the determination of total mercury in aquatic life. Water Air Soil Pollut. 172:359–374. Houserova P, Hednavny J, Matejicek D, Kracmar S, Sitko J, Kuban V. 2005. Determination of total mercury in muscle, intestines, liver and kidney tissues of cormorant (Phalacrocorax carbo), great crested grebe (Podiceps cristatus) and Eurasian buzzard (Buteo buteo). Vet Med Czech. 50:61–68. Ikem A, Egilla J. 2008. Trace element content of fish feed and bluegill sunfish (Lepomis macrochirus) from aquaculture and wild source in Missouri. Food Chem. 110:301–309. Krishna MVB, Ranjit M, Karunasagar D, Arunachalam J. 2005. A rapid ultrasound-assisted thiourea extraction method for the determination of inorganic and methyl

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mercury in biological and environmental samples by CV AAS. Talanta. 67:70–80. Maggi C, Berducci MT, Bianchi J, Giane M, Campanella L. 2009. Methylmercury determination in marine sediment and organisms by Direct Mercury Analyzer. Anal Chim Acta. 641:32–36. McClain WC, Chumchal MM, Drenner RW, Newland LW. 2006. Mercury concentrations in fish from lake Meredith, Texas: implications for the issuance of fish consumption advisories. Environ Monit Assess. 123:249–258. Rahman GMM, Kingston HM. 2005. Development of a microwave-assisted extraction method and isotopic validation of mercury species in soils and sediments. J Anal At Spectrom. 20:183–191. Ribeiro AS, Vieira MA, Curtius AJ. 2004. Slurry sampling for Hg determination in sediments, sewage sludge and coal samples by cold vapor atomic absorption spectrometry. J Braz Chem Soc. 15:825–831. Rodrigues JL, Torres DP, Souza VCO, Batista BL, Souza SS, Curtius AJ, Barbosa Jr F. 2009. Determination of total and inorganic mercury in whole blood by cold vapor inductively coupled plasma mass spectrometry (CV ICP-MS) with alkaline sample preparation. J Anal At Spectrom. 24:1414–1420. Tao G, Willie SN, Sturgeon RE. 1998. Determination of total mercury in biological tissues by flow injection cold vapour generation atomic absorption spectrometry following tetramethylammonium hydroxide digestion. Analyst. 123:1215–1218. Tao G, Willie SN, Sturgeon RE. 1999. Determination of inorganic mercury in biological tissues by cold vapor atomic absorption spectrometry following

tetramethylammonium hydroxide solubilization. J Anal At Spectrom. 14:1929–1931. Torres DP, Borges DLG, Frescura VLA, Curtius AJ. 2009. A simple and fast approach for the determination of inorganic and total mercury in aqueous slurries of biological samples using cold vapor atomic absorption spectrometry and in situ oxidation. J Anal At Spectrom. 24:1118–1122. Torres DP, Frescura VLA, Curtius AJ. 2009. Simple mercury fractionation in biological samples by CV AAS following microwave-assisted acid digestion or TMAH pre-treatment. Microchem J. 93:206–210. Torres DP, Vieira MA, Ribeiro AS, Curtius AJ. 2005. Determination of inorganic and total mercury in biological samples treated with tetramethylammonium hydroxide by cold vapor atomic absorption spectrometry using different temperatures in the quartz cell. J Anal At Spectrom. 20:289–294. US Environmental Protection Agency (USEPA). 2007. U.S. EPA Method 7473 (2007) Mercury in solids and solutions by thermal decomposition, amalgamation, and atomic absorption spectrophotometry. Washington (DC): USEPA. Vieira MA, Ribeiro AS, Curtius AJ, Sturgeon RE. 2007. Determination of total and methylmercury in biological samples by photochemical vapor generation. Anal Bioanal Chem. 388:837–847. Welz B, Sperling M. 1999. Atomic absorption spectrometry. Weinheim (Germany): Wiley-VCH. Willie SN, Gre´goire DC, Sturgeon RE. 1997. Determination of inorganic and total mercury in biological tissues by electrothermal vaporization inductively coupled plasma mass spectrometry. Analyst. 122:751–754.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 633–640

Validation of an analytical method for the determination of cadmium (Cd) in fish by atomic absorption spectrometry with electrothermal atomisation L.C.S.M. Costa, A.P.N. Neto*, M.Q. Arau´jo, M.C.C. Melo, D.M.S. Furtado and A.N.S. Kikuchi Laboratory of Residues and Contaminants – National Laboratory of Ministry of Agriculture, Livestock and Food Supply (LANAGRO-PA), Av. Almirante Barroso, 1234 Zip Code: 66095-000, Brazil (Received 25 November 2010; final version received 16 December 2011) The validation of an analytical method was carried out for the determination of cadmium (Cd) in fish. The method was based on sample digestion in a microwave oven and subsequent reading using an atomic absorption spectrometer with a graphite furnace. The factorial design of experiments was applied to assess method ruggedness using the methodology of Box et al. [Box GEP, Hunter WG, Hunter JS. 1978. Statistics for experiments: an introduction to design, data analysis and model building. New York (NY): Wiley], studying the influence of sample mass, volume and concentration of acid used for sample digestion and the volume of modifier used. To study the possible matrix effect in the determination of Cd, the standard addition method was also performed. The results were treated using the OLS method. For the normality test a homoskedastic distribution was observed for the developed method and the results were adjusted to the statistical model proposed. F-tests and Student’s t-tests indicated that there was no matrix effect on the calibration curve between the concentration range 1.0–10.0 mg Cd l–1. Parameters such as selectivity, precision, decision limit, detection capability and limit of quantification were established by the method of standard addition to blank samples. The limit of quantification was 6.8 mg kg–1. Accuracy, which was evaluated by using a certified reference material, was 107.0%. The recovery of the spiked analyte was 93.69% for the concentration of 50 mg kg–1. Precision was defined by the coefficient of variation observed (Horrat value), estimated in terms of repeatability and reproducibility, and the values were below the limit, which is 2.0. The validation procedure confirmed the suitability of the method. Keywords: GFAAS; heavy metals – cadmium; heavy metals; fish

Introduction The risks of poisoning by high concentrations of heavy metals in food has been gaining more prominence in recent decades, requiring responsible agencies to get a better match of critical limits for the concentrations of these metals in the environment, considering the ecological damage they cause (Vries et al. 2007). The pollution of aquatic environments has become one of the biggest problems of the planet. Among the pollutants, metals in particular are a major concern because of potential toxic effects, indestructibility and their bioaccumulability (O¨ztu¨rk et al. 2009). Relatively small amounts of cadmium (Cd) are found in the environment naturally, from forest fires and volcanic emissions, but mostly Cd is released by human activities such as mining operations, fuel combustion and the disposal of manufactured products containing the metal (Public Health Statement for Cadmium 2010).

Cd from industrial waste has a large negative effect on the health of animals and man. This metal, when introduced into the body, is accumulated in the liver and kidneys. Human exposure to Cd and other toxic metals has been reduced because of the political world agro-food waste control, which regulates and determines the maximum levels of heavy metals in these products (Szkoda and Zmudzki 2005). According to Regulation EC 1881 of 19 December 2006, which sets the maximum levels for certain contaminants in foodstuff, the maximum quantities of Cd in fish varies with species, being 0.050 mg kg–1 for species which are not predators and 0.10 mg kg–1 for predatory species (European Commission 2006). The aim of this study was to validate a new method for the determination of Cd in fish muscle, since this is part of human diet in this region of Brazil. The data and statistical calculations were all based on the established by Directive 657 of 12 August 2002

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and Directive 333 of 28 March 2007, both from the European Community (European Commission 2002, 2007). The results obtained in this study will provide information about the quantities of Cd in fish species from the region, which contributes to the effective control of environmental quality and consequently the health of organisms inhabiting the ecosystem.

Materials and methods Materials, reagents and solutions An analytical balance (Crystal 200, Gibertini, Novate, Italy) was used for weighing the samples. High-purity water (resistivity ¼ 18.2 M cm) obtained by a purification system (Ultrapurelab ULTRAMK@, Elga LabWater, High Wycombe, UK) was used to prepare all aqueous solutions and dilutions. HNO3 analytical grade (Sigma Aldrich, Buchs SG, Switzerland), used in the digestion of the sample, was purified by a distillation system (Milestone, Model Duopur, Sorisole, Italy). Hydrogen peroxide, analytical grade (Merck, Darmstadt, Germany), was used in the digestion of samples of fish. The fish used for the development and validation of analytical methodology were acquired by the Inspection Service and Animal Health (SISA) from Para´ state. The species studied were Colossoma macropomum, Cynoscion leiarchus and Cynoscion acoupa. All solutions used in the method were stored in high-density polypropylene bottles. The apparatus such as the auto-sampler cups, ferrules, micropipettes and glass materials (beakers, flasks, pipettes) were cleaned by soaking in HNO3 (purified) 10% v/v for 24 h. After the immersion in nitric acid, the vessels were thoroughly rinsed five times with Milli-Q water, dried and stored protected from contamination. A solution of Na2WO4 2H2O (Sigma Aldrich, Seelze, Germany) 1.0 g l–1 was prepared by dissolving 0.1794 g of reagent in 100 ml of Milli-Q water. A solution of (NH4) 3RhCl6.1 0.5H2O (Sigma Aldrich, Milwaukee, WI, USA) 0.8 g l–1 was obtained by dissolving 0.0031 g of reagent in 10 ml of HCl 10% v/v. A solution of Mg (NO3)2 6H2O (ChemScan, Grindalsvn, Elverum, Norway) was used as the matrix modifier. The solution of Cd 1000 mg l–1 (Sigma Aldrich, TraceCERT, Buchs SG, Switzerland) was used as the standard in the tests.

Apparatus The microwave digestive equipment Multiwave 3000 (Anton Paar, Graz O¨sterreich, Austria) was used for digestion of the samples. The equipment consists of a rotor HF100 with capacity for 16 digestion vessels (Teflon). Pressure and temperature were simultaneous monitored in the reaction vessels. A Perkin Elmer Model AA800 (Norwalk, CT, USA) atomic absorption spectrometer, equipped with a Zeeman background correction, a EDL lamp, a graphite furnace with transverse heating of the graphite tube (THGA) with integrated platform and an autosampler AS-800 was used for the Cd analysis. The instrumental parameters for the technique are given in Table 2. The calibration curve used for the analytical methodology ranged from 1.0 to 10.0 mg l–1, equivalent to 10–100 mg kg–1 of Cd. The calibration solution was prepared by diluting the standard solution of Cd in HNO3 0.2% v/v.

Procedure of digestion Fish samples were triturated, homogenized, freeze dried and then kept in a clean, dry container. 2.000 ( 0.0009) g of fish were weighted in an analytical balance and transferred to the vessel digestion samples, then 7 ml of HNO3 (purified) 55% and 2 ml H2O2 were sequentially added into the digestion vessel. Afterwards, the Teflon vessels were closed and added to the rotor of the microwave oven. The decomposition of organic matter was performed in closed vessels and assisted by microwave radiation, equipped with a probe for the internal temperature monitoring control of the samples. The sequence of steps proposed to sample digestion is established in Table 1. After the digestion process, the vessels were taken to a freezer for 30 min to cool to room temperature. The digested sample was transferred to Falcon tubes and diluted to a final volume of 20 ml by adding high purity deionised water with a specific resistance 18 M .

Table 1. Programme for sample digestion for the determination of Cd in fish.

Certified reference material

Step

The certified reference material used was DORM-3Õ (Fish Protein Certified Reference Material for Trace Metals) from the National Research Council of Canada.

1 2 3 4

Power (W)

Ramp (min)

Time (min)

Ventilation

0–1000 1000–1300 1300–1000 1000–0

3 2 20 10

5 10 10 0

1 1 1 3

Food Additives and Contaminants Instrumental conditions The AAS program used for the measurements was: wavelength ¼ 228.8 nm, signal absorbance slit width ¼ 0.7 nm, lamp current ¼ 63–69 mA and background correction. The graphite tubes used were permanent modified according to Lima et al. (1998) and Lima, Barbosa, Brasil, et al. (2002), employing solutions Na2WO4 2H2O and (NH4) 3RhCl6 10.5H2O. A volume of 20 ml of digested sample and 10 ml of solution of matrix modifier (magnesium nitrate hexahydrate-Mg(NO3)2 6H2O) were injected on the L’vov platform inside the graphite tube by the auto-sampler needle. Instrumental settings for AAS are reported in Table 2.

Validation parameters Several parameters were taken into account and evaluated for the in-house validation of the method, namely: range of linearity, selectivity, recovery at six levels of concentration, trueness by CRM, repeatability and within-laboratory reproducibility at three levels of concentration, instrumental/method detection limits (LOD), quantification limits (LOQ), limit of decision (CC ), and detection capability (CC ). Furthermore, a ruggedness study of the method was carried out. In this study the definitions and procedure for validation parameters were applied according to European Union standards for foodstuff (European Commission 2002, 2007).

Results and discussion Method optimisation Several experiments were conducted to establish the best conditions for all analytical parameters. An optimisation study to evaluate the best parameters for the microwave digestion of fish as well as the adequacy of all parameters (drying, pyrolysis and atomisation) of the atomic absorption spectrometer were performed. Methodological conditions for both sample digestion, considered the first stage of analysis,

Table 2. Instrumental conditions for the determination of Cd in fish muscle.

Step 1 2 3 4 5

Temperature ( C)

Ramp time (s)

Hold time (s)

Gas flow rate (ml min–1)

150 200 450 1250 2450

2 5 5 0 1

25 15 40 4 3

250 250 250 0 250

635

and for determining the analyte itself are very important in the analysis of Cd (Lima et al. 1999; Vinas et al. 1999; Baldini et al. 2000; Farmer and Farmer 2000; Kim 2004). To degrade the organic matter, HNO3 solution (purified) 55% was used. In closed systems using microwave equipment the use of these digestion conditions is quite favourable, since the process consists of the decomposition of NOx compounds to form nitric acid. The acid reacts with oxygen present in the digestion vessel and through other chemical reactions forms nitric acid again. This fact increases the oxidising power of the solution. Another major advantage of using dilute nitric acid is the considerable decrease in the levels of contaminants in the blank sample solution (Nobrega 2010). Another factor that has gained prominence in the positive use of the method is the use of W–Rh permanent modifier combined with the chemical modifier Mg (NO3) 6H2O. In recent years permanent modifiers have been widely applied to analysis using the technique of graphite furnace. The advantages compared with conventional techniques are methods with programs that are simpler and faster, the reduction or elimination of volatile impurities during the treatment of the graphite tube, the achievement of better detection limits, the analytical signal remains stable for longer, a reduction in the number of calibrations on an analytical routine notoriously long, and the improvement the life of the tube, which reduces analytical costs (Lima, Barbosa, Krug, et al. 2002). Validation of the Cd method was tested by analysing the certified reference material (trueness parameter) (Table 3).

Method validation Validation was performed to ensure the reliability of the method. This process complies with Directive 657 and Directive 333 of the European Commission concerning the performance of analytical methods and the interpretation of results. The parameters include linearity of the calibration curve, selectivity, limit of detection (LOD), limit of quantification (LOQ), accuracy, recovery, precision, decision limit

Table 3. Comparison of the determined and certified for Cd in the reference material.

Material NRC-CNRC DORM-3Õ

Certificate value (mg kg–1)

Given value (mg kg–1)

Recovered (%)

0.290 0.020

0.311 0.0059

107.40

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(CC ), decision capacity (CC ), and measurement uncertainty. According to Regulation (EC) No. 1881/ 2006 of the European Community of 19 December 2006, which sets maximum levels for certain contaminants in foodstuffs, the maximum level of Cd for the matrix fish varies with the species, being 50 mg kg–1 for non-predator fish and 100 mg kg–1 for predatory species (European Commission 2006). From this information a study of the robustness of the method was performed, since according to the species of fish used (predator or non-predator) the mass of the sample needs to be changed.

the DEO. The dependent variable measured was the recovery. The statistical analysis was performed using STATISTICA version 6.0. To determine the statistical significance of the effects of input variables in the response recovery (R), an analysis of variance was performed, as indicated in Table 5. It shows the effects of factors on the response in the linear (main effect) and with cross terms (interaction effect), which can be evaluated based on the statistics F and Pr. The results in Table 5 show that the independent variable X4 (volume of modifier), is statistically significant for the response at a significance level of 0.05. The isolated variables X1 (mass of sample), X2 (acid concentration), X3 (volume peroxide) and combinations of all variables are not statistically significant to the response R. The volume of the modifier used exerted considerable influence on recovery. In Figure 1 the effect of the variable X4 on the process can be confirmed. This result demonstrates that the variations in the mass of the sample, nitric acid concentration and volume of hydrogen peroxide have no effect on the results of the method in the ranges studied.

Ruggedness The method ruggedness under conditions of major changes was evaluated by using a design of experiments (DEO) according to the methodology of Box et al. (1978). The independent variables studied and their respective levels are given in Table 4. Sixteen tests were performed in duplicate, in the case of a planning 24. The experiments were carried out with spiking fish samples at 50 mg kg–1, which is the maximum Cd level for muscle meat of fish, established by the European Union and eight factors were chosen as variables for

Linearity of calibration curve Method linearity was performed by six series of analysis obtained on different days by the injection of Cd standard solution at five levels of concentration: 1.0, 2.5, 5.0, 7.5 and 10.0 mg l–1 corresponding to 10, 25, 50,75 and 100 mg kg–1 in the matrix. Prior to the calculation of the regression line and relevant parameters the absence of outliers was controlled by the Jacknife standardised residuals test and Grubs test and any measurement was eliminated. The calibration curve, obtained by plotting the peak area of the six series of analyses, expressed in absorbance units versus concentration in the range

Table 4. Factors and levels studied in the experimental design.

Variables

Codes

Low level

High level

X1 X2 X3 X4

1.0 55 2.0 10

2.0 65 2.5 15

Sample mass (g) Concentration of HNO3 Volume of H2O2 (ml) Volume of Mg(NO3) 6H20 (ml)

Table 5. Analysis of variance (ANOVA) for recovery.

Effect

Sum of squares

Degrees of freedom

Mean square

F-test

(Pr)

X1 X2 X3 X4 X1 X2 X1 X3 X1 X4 X2 X3 X2 X4 X3 X4 Lack of fit Pure error Total (correlation)

51.84 0.49 2.98 105.06 40.01 8.70 0.001 1.82 0.46 13.32 51.84 40.06 264.73

1 1 1 1 1 1 1 1 1 1 1 5 15

51.40 0.49 2.98 105.07 40.01 8.70 0.001 1.82 0.46 13.32 51.84 8.01 –

6.47 0.06 0.37 13.11 4.99 1.09 0.00008 0.22 0.06 1.66 6.47 – –

0.05 0.81 0.57 0.01 0.08 0.34 0.99 0.65 0.82 0.25 0.05 – –

Food Additives and Contaminants 1.0–10 mg l–1, gave the linear regression equation y ¼ 0.0632x þ 0.007 with a determination coefficient (r2) of 0.999. Outliers were not detected in curve 1 by Grubbs and Jacknife standardised residuals tests. The residual plot for the solvent curve is shown in Figure 2.

Selectivity Matrix effects were checked by applying the method of standard additions. Three calibration curves were Table 6. Parameters of the OLSM fit for curves 1, 2 and 3.

Curve 1 2 3

Interception value

Slope

0.0007 0.0089 0.0092

0.0632 0.0618 0.0611

0.9990 0.9997 0.9998

637

prepared at the levels of 1, 2.5, 5, 7.5 and 10 mg l–1, in six independent replicates, and run in a random order. The first (solvent curve) was prepared according to the procedure in linearity assessment. The second (matrixmatched curve before digestion) used fish-spiked samples at concentrations of 10, 25, 50, 75 and 100 mg kg–1. Cd solution was added to the matrix before the samples were digested at the microwave. The third (matrix-matched curve after digestion) used spiked samples at the same concentration of curve 2, but the Cd solution was added to the fish after the digestion step. Blank controls of the fish samples were analysed in the digestion batches of curves 2 and 3. The slope, intercept and respective variances of both curves were calculated by the ordinary least squares method (OLSM); the results are shown in Table 6. The data obtained for the replicates of each level were compared using the F-Snedocor test to evaluate the homogeneity of variances. The averages between

Figure 1. Individual and combined effects for the response.

Curve 1 0.025 0.02 0.015 0.01

0.005 0

–0.005

–0.01 –0.015 –0.02 –0.025

Cd (µg.L–1)

Figure 2. Residual plots for outlier treatment; ei ¼ residual, ¼ non outlier. Dotted lines correspond to (t0.95,n – 2)sres variation, where sres is the standard deviation of residues.

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Table 7. Results of the F-test (Snedocor) and Student’s t-test in a comparison between curve 1 (analyte solution), curve 2 (matrix matched before digestion) and curve 3 (matrixmatched after digestion).

that the European Commission has adopted for fish muscle (European Commission 2006).

Level (mg kg–1)

Evaluation of trueness

Curves 1 and 2 10 25 50 75 100 Curves 1 and 3 10 25 50 75 100 Curves 2 and 3 10 25 50 75 100

F calculated

t calculated

1.54 1.14 1.39 4.99 4.70

0.85 0.71 1.68 1.60 1.60

2.88 2.94 4.92 4.95 4.45

1.39 1.02 1.07 1.55 1.76

4.44 2.58 3.55 1.01 1.06

0.29 1.31 0.72 1.04 1.64

Note: Critical value from the F distribution: F(0,95,n–1,n–1) ¼ 5.05. Critical value from the t distribution: t(0,95,n1þn2–2) ¼ 1.81.

the results obtained were compared using the Student’s t-test. The results for the comparison between curves 1, 2 and 3 are presented in Table 7. Based on these results it was possible to conclude that Cd solvent solutions gave equivalent absorbance signal as the fish muscle sample containing the same concentration of the metal. This observation confirms that there is no matrix interfering and that microwave digestion using high-pressure closed-vessels could provide a complete digestion of the investigated samples.

Evaluation of the limit of detection (LOD) and limit of quantification (LOQ) As provided in Directive 333 of the European Community, the LOD is defined as the smallest measured content from which it is possible to deduce the presence of the analyte with reasonable statistical certainty. In the regulation of the European Community the LOQ is defined as the lower level from which the analyte can be measured with reasonable statistical certainty. The values were calculated from the standard deviation of the results of 21 fish blank samples spiked at a concentration of 1.0 mg l–1. LOD and LOQ values were 0.24 and 0.68 g l 1, respectively, corresponding to 2.4 and 6.8 mg kg 1 in matrix. The LOQ is satisfactory with respect to acceptance criteria established at 5.0 and 10 mg kg 1

Trueness was established by assays with CRM DORM 3Õ in seven independent replicates. The samples were split into different batches and solvent curves were prepared to calculate the analyte concentration in the spiked samples. The result is shown in Table 3. According the criteria suggested by Directive 657 European Community for mass fractions, the minimum trueness ranged from 80% to 110%. Based on the acceptability criteria, no lack of trueness were observed.

Evaluation of precision Precision was determined based on the established in Directive 657 European Community by performing tests on three sets of blank fish samples (six replicates each) fortified with Cd at concentrations around the maximum contaminant level. The fortified levels corresponded to 0.2, 1.0 and 2.0 the maximum level (i.e., 10, 50 and 100 mg kg 1). Samples were analysed on 3 different days with the same instrument, but by two different operators, corresponding to a total number of 54 samples. Precision was calculated either in terms of repeatability (RSDr), the variability of independent test results obtained with the same method on identical test items in the same laboratory by the same operator using the same equipment, or in terms of withinlaboratory reproducibility (RSDR), the variability of independent test results obtained with the same method on identical test items in the same laboratory by different operators in different time, using the same equipment. Directive 333 of the European Commission issued that the permitted value of experimental RSD for each concentration must be below twice the value derived by the Horwitz equation, which provides the expected RSD% only on the basis of the concentration, independently of the matrix and analytical method used. This regulation also establishes performance criteria for precision based on the Horrat value, which should be less than 2 under repeatability and reproducibility conditions. The Horrat value corresponds to the observed RSDr and RSDR divided by the RSD estimated from the Horwitz equation. As can be seen in Table 8, both RSDR and RSDr values are lower than calculated repeatability by the Horowitz equation; this result indicates that the method satisfies minimum performance criteria established by Directive 333 of the European Commission. The results also indicate Horrat values lower than 2.

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Table 8. Repeatability and within-laboratory reproducibility for the determination of cadmium in spiked fish samples.

Rating limit decision (CCa) and detection capability (CCb)

Fortification level (mg kg–1)

CC was defined as the limit above which samples were concluded to be non-compliant, with an error probability of 5%; CC was defined as the smallest content of the substance that may be detected, identified and/or quantified in a sample with an error probability of 5%. The critical concentration for maximum contaminant level (MCL) compliance (CC , ¼ 0.05) was calculated, according to Directive 657 of the European Community from the MCL values of 0.050 and 0.1 mg kg 1 plus 1.64 times the SD of 20 fortified samples at the MCL. CC was obtained by adding to CC 1.64 times the same SD. CC and CC values, calculated as above, were 0.058 and 0.067 mg kg 1, respectively, for the MCL of 0.050 mg kg 1. Considering the 0.1 mg kg 1 MCL, the values were 0.104 and 0.108 mg kg 1.

100

Parameter

Op 1

Op 2

Overalla

Mean (mg kg–1) SD (mg kg–1) RSD (%)a Horrat value Mean (mg kg–1) SD (mg kg–1) RSD (%) Horrat value Mean (mg kg–1) SD (mg kg–1) RSD (%) Horrat value

12.68 1.56 12.28 0.38 49.05 3.52 7.17 0.29 99.62 3.31 3.33 0.15

12.70 1.37 10.77 0.34 45.54 3.37 7.41 0.29 98.55 3.95 4.01 0.18

12.69 1.44 11.38 0.36 47.29 3.85 3.67 0.15 99.08 3.64 8.10 0.36

Note: Op, operator (relative standard deviation (RSD) under repeatability conditions (RSDr)); aRSD under within-laboratory reproducibility conditions (RSDR); SD, standard deviation.

Table 9. Recovery results. Level (mg kg–1) 10 25 50 75 100

Recovery (%) 95.35 98.33 93.69 93.89 90.24

Evaluation of recovery Five levels of additions were selected corresponding to 10, 25, 50, 75 and 100 mg kg–1 of Cd in the matrix, in six independent replicates. Consequently, independent aliquots of fish were spiked with the right amount of standard solution of Cd, in five different levels of concentration, according to the scheme shown in Table 9, so as to obtain 30 spiked samples and six unspiked samples in order to quantify the natural level. All samples were digested according to the preestablished microwave programme and then analysed. The recovery was calculated as follows: 100: C Recovery ð%Þ ¼ ð1Þ spiked concentration where C is the element concentration found. An acceptance limit between 90% and 110% was selected in compliance with Directive 657 of the European Commission. The results for the recovery tests were particularly successful being in the acceptance range 90–110%, especially considering the thermal microwave digestion programme used for fish samples.

Conclusion A method for determination of cadmium using the technique of graphite furnace spectrometry was validated according to the requirements set by the European Union. The validation results indicate that the method is precise and accurate and that the limit of quantification meets the criteria set out in Regulation 2007/333/EC. Therefore, the method is suitable for use on official control, because the quality and comparability of analytical results can be ensured.

Acknowledgements To CNPQ for a scholarship support programme; and to National Agriculture Laboratory at Para State/MAPA by funds and facilities.

References Baldini M, Stacchini P, Miniero R, Paodi P, Facelli P. 2000. Cadmium in organs and tissues of horses slaughtered in Italy. Food Addit Contam. 17:679–687. Box GEP, Hunter WG, Hunter JS. 1978. Statistics for experiments: an introduction to design, data analysis and model building. New York (NY): Wiley. European Commission. 2002. Commission Decision of 12 August (2002) implementing Council Directive 96/23/EC concerning the performance of analytical methods and interpretation of results. Off J Eur Comm. L. 221:8–36. European Commission. 2006. Commission Decision N 1881/2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs. Off J Eur Comm. L. 364:5–24. European Commission. 2007. Commission Decision N 333/ 2007 of 28 March 2007 laying down methods of sampling and analysis for the official control of levels of lead,

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cadmium, mercury, inorganic tin, 3-MCPD and benzo (a) pyrene in foodstuffs. Off J Eur Comm. L. 88:29–38. Farmer AA, Farmer AM. 2000. Concentrations of cadmium, lead and zinc in livestock feed and organs around a metal production center in eastern Kazakhstan. Sci Total Environ. 257:53–60. Kim M. 2004. Determination of lead and cadmium in wines by graphite furnace atomic absorption spectrometry. Food Addit Contam. 21:154–157. Lima EC, Barbosa RV, Brasil JL, Santos AHDP. 2002. Evaluation of different permanent modifiers for the determination of arsenic, cadmium and lead in environmental samples by electrothermal atomic absorption spectrometry. J Anal At Spectrom. 17:1523–1529. Lima EC, Barbosa FJ, Krug FJ, Tavares A. 2002. Copper determination in biological materials by ETAAS using W–Rh permanent modifier. Talanta. 57:177–186. Lima EC, Krug FJ, Ferreira TA, Barbosa FJ. 1999. Tungsten-rhodium permanent chemical modifier for cadmium determination in fish slurries by electrothermal atomic absorption spectrometry. J Anal At Spectrom. 14:269–274. Lima EC, Krug FJ, Jackson KW. 1998. Evaluation of tungsten-rhodium coating on an integrated platform as a permanent chemical modifier for cadmium, lead, and selenium determination by electrothermal atomic

absorption spectrometry. Spectrochim Acta B. 53:1791–1804. Nobrega JA. 2010. Preparo de amostra assistido por radiaçaõ micro-ondas: uma breve visaõ de desenvolvimentos recentes. Workshop sobre preparo de amostras. 8th ed. Saõ Paulo (Brazil): Instituto de quı´ mica – USP. O¨ztu¨rk M, O¨zo¨zen G, Minareci O, Minareci E. 2009. Determination of heavy metals in fish, water and sediments of Avsar Dam lake in Turkey. Iran J Environ Hlth Sci Eng. 6:73–80. Public Health Statement for Cadmium. 2010; [cited 2010 Oct 13]. Available from: http://www.atsdr.cdc.gov. toxprofiles/phs5.html/ Szkoda J, Zmudzki J. 2005. Determination of lead and cadmium in biological material by graphite furnace atomic absorption spectrometry method. Bull Vet Inst Pulawy. 49:89–92. Vinas P, Pardo MM, Campillo N, Hernandez CM. 1999. Fast determination of lead and copper in dairy products by graphite furnace atomic absorption spectrometry. J AOAC Int. 82:368–373. Vries W, Ro¨mkens PF, Schu¨tze G. 2007. Critical soil concentrations of cadmium, lead, and mercury in view of health effects on humans and animals. Rev Environ Contam Toxicol. 191:91–130.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 641–656

Within-laboratory validation of a multiresidue method for the analysis of 98 pesticides in mango by liquid chromatography-tandem mass spectrometry N. Fleury Filho*, C.A. Nascimento, E.O. Faria, A.R. Cruvinel and J.M. Oliveira Residues and Contaminants Laboratory – LANAGRO-GO, Ministry of Agriculture, Livestock and Food Supply, Goiaˆnia, Goia´s, CEP 74674-025, Brazil (Received 29 November 2010; final version received 13 July 2011) A within-laboratory validation procedure for a selective and sensitive method for the simultaneous determination of 98 pesticide residues in mango is presented. QuEChERS extraction was adapted to laboratory conditions. Mango samples (10 g) mixed with sodium sulfate (4 g) and sodium acetate (1 g) were extracted with acetonitrile/ acetic acid (99/1 v/v), cleaned using dispersive solids, and subsequently identified and quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Pesticides were separated on a reversed-phase column using a gradient elution in conjunction with positive-mode electrospray ionisation. The analytical performance of the method was demonstrated by analysis of spiked mango samples at three concentration levels (0.01, 0.05 and 0.1 mg kg 1) for 3 different days, and the analysis was performed by three analysts. Calibration curves were statistically acceptable by the ordinary last-square method (OLSM), with a regression coefficient above 0.98 for all analytes. The method accuracy (n ¼ 18) was between 80% and 110%, and precisions were below 20% for 95% of the analytes. The method uncertainty at the LOQ was evaluated considering the uncertainty associated with the calibration curve and the uncertainty associated with the method precision. The validation data for all pesticides were in accordance with Brazilian and European guidelines for pesticide residue analysis. Keywords: chromatography – LC/MS; in-house validation; method validation; LC/MS; pesticide residues; residues; fruit; vegetables

Introduction The worldwide commerce of products has increased significantly with economic globalisation and the variety of agricultural products has been extended considerably over the last 10 years (Ministe´rio da Agricultura, Pecua´ria e Abastecimento (MAPA) 2009). The diversified, modern and efficient agriculture developed in Brazil has given the nation the status of one of the biggest food suppliers in the world, representing 26.5% of the national economy in 2009 (MAPA 2010). Brazilian agriculture produces annually about 54 million tonnes of fruit and vegetables and has exported more than 870 tonnes of fruit in 2009 (MAPA 2010). Currently, Brazil is the third largest fruit producer in the world, behind China (157 million tons) and India (54 million) (Revista Online Brasil Alimentos 2009). To maintain fruit and vegetable production, a wide variety of pesticides are commonly applied to agricultural crops in order to increase yield and produce highquality products. However, pesticides can remain in vegetables as residues, after harvesting being frequently detected. In consequence, the control of pesticide residues by monitoring programmes is currently an increasing concern for producers, traders and *Corresponding author. Email: nelio.fleury@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.606230 http://www.tandfonline.com

consumers. In this sense, international organisations and governments have established maximum residue limits (MRLs) for each compound and commodity to ensure food safety (Moreno et al. 2008). The Brazilian government has a duty to ensure that the products are compliant with safety and quality criteria required by consumers. An important tool to monitor closely and ensure this compliance in Brazil is the National Residue Control Plan (NRCP) established by the Ministry of Agriculture, Livestock and Food Supply. A laboratory network operating under an NRCP is crucial to provide valuable data for the evaluation of this potential exposure (Maurı´ cio et al. 2009). Reliable low-cost and effective multiresidue analytical methodologies must be employed at the laboratory, being capable of residue measurement at very low levels and also provide evidence to confirm both the identity and quantity of any pesticide residues detected. Chromatographic techniques coupled to mass spectrometric detection have usually been applied for the determination of pesticides residues in food samples. In recent years, LC has emerged as an excellent technique, especially for the analyses of polar and thermolabile pesticides that are not readily amenable

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to GC or require derivatisation before GC analysis (Hiemstra and de Kok 2007). LC with triple quadruple mass spectrometry (LCMS/MS) in multi-reaction monitor mode (MRM) is widely used in multiresidue analysis because of its high sensitivity and selectivity (Pan et al. 2008). The application of this technique in multi-class pesticide residue analysis has made the multiresidue analysis more rugged and convenient by offering the possibility of simultaneous determination of a large number of pesticides with varied physico-chemical properties without the need for baseline separation. It has minimised the requirement for extensive sample clean-up, which was otherwise essential in earlier methods of analysis (Banerjee et al. 2007). A method that meets the requirements of a modern chromatographic technique and a simple and fast extraction procedure is the QuEChERS (quick, easy, cheap, effective, rugged and safe) method, which has been readily accepted by many pesticide residue analysts in the past few years. Some modifications to the original QuEChERS method have to be introduced to ensure efficient extraction of pH-dependent compounds, to minimise degradation of susceptible compounds and to expand the spectrum of matrices covered (Anastassiades 2006). Many papers have been recently published using this technique or a variation of it (Anastassiades et al. 2003; Lehotay, de Kok, et al. 2005; Lehotay, Mastoska, et al. 2005, 2010; Anastassiades 2006; Dı´ ez et al. 2006; Banerjee et al. 2007; Ferrer and Thurman 2007; Shimelis et al. 2007; Wang et al. 2007; Moreno et al. 2008; Gilbert-Lo´pez et al. 2010; Koesukwiwat et al. 2010). Nevertheless, an analytical procedure can only be said to be fit-for-purpose when a laboratory statistically proves its efficiency under its own conditions, which usually occurs during an intra-laboratory validation procedure. There are many method validation guides for residue analysis in food matrix (Eurachem 1998; European Commission 2002; Thompson et al. 2002; Instituto Nacional de Metrologia, Normalizaçaõ e Qualidade Industrial (INMETRO) 2007). A wellknown guide for pesticides analysis in fruit and vegetables is SANCO (2010). Due to that guide (SANCO 2010) presenting a general and non specific procedure the laboratory must establish some clear, objective and statistically proved validation procedure in order to demonstrate that the method is fit for purpose. This paper presents a robust and statistically proven intra-laboratory validation procedure for the analysis of 98 pesticides and their metabolites in mango by acetate-buffered QuEChERS extraction followed by LC-MS/MS detection and quantification. All 98 pesticides were selected according to the Brazilian monitoring programme (Instruçaõ Normativa No. 26 2010).

By the end of the validation procedure it was possible to estimate the limit of quantification (LOQ), limit of detection (LOD), linearity, specificity, ion ratio reproducibility, accuracy, precision and the method uncertainty, complying with Brazilian (Instruc¸a˜o Normativa No. 24 2009) and European Legislation (SANCO 2010).

Material and methods Chemicals and reagents Pesticide reference standards were obtained from Riedel-de-Hae¨n (Seelze, Germany) and Dr Ehrenstorfer (Augsburg, Germany). Pure standards were stored at 4 4 C. All organic solvents used were HPLC grade. Methanol was obtained from Merck (Darmstadt, Germany) and acetonitrile was purchased from Carlo Erba (Milano, Italy). Anhydrous sodium sulfate was obtained from Merck (Darmstadt, Germany); anhydrous sodium acetate was obtained from Vetec (Rio de Janeiro, Brazil) and ammonium formate was obtained from Acros Organics (New Jersey, USA). Acetic acid HPLC grade was from Vetec (Rio de Janeiro, Brazil); formic acid was acquired from JT Baker (New Jersey, USA). Deionized water was generated by a water purification system (Simplicity UV, Millipore). PSA (primary secondary amine) sorbent (40 mm particle size) and Bondesil C-18 (40 mm particle size) were obtained from Varian (California, USA). Individual pesticide stock solutions at 1000 mg l 1 were prepared in methanol, toluene, acetone or acetonitrile, according to their solubility, and stored at 18 C. Stock standard solutions of carbendazim and simazim were prepared at 200 mg l 1 in methanol and acetone, respectively. All stock solutions were doubly prepared and checked by an accuracy test; solutions that differ more than 5% were prepared again. A mixed working standard solution, containing 5 mg l 1 of each pesticide in acetonitrile was prepared from individual stock solutions. This solution was used as a spike solution for validation experiments and QC samples. A dilution of the mixed working solution was performed to give a solution at 200 mg l 1. This solution was used in the preparation of the matrixmatched calibration curve (MMCC) (corresponding to samples at 10, 30, 50, 70, 80, 100, 120 and 150 mg l 1). The mixed solutions were stored in a freezer at 18 C.

Samples Organic mangos obtained from the National Agricultural Laboratory in Goia´s (LANAGRO-GO)

Food Additives and Contaminants orchard were used as a blank sample in the validation experiments and for the preparation of MMCCs. The mango samples were previously determined to be free of pesticide residues.

Extraction A slightly modified buffered QuEChERS method (Lehotay, Mastojka´ et al. 2005) was used for sample extraction. A portion (10 g) of homogenised blank mango was weighed in a 50 ml polypropylene centrifuge tube. Blank samples were spiked with known volumes of a working standard solution to work as quality control samples and to evaluate the method recovery and precision during the validation procedure. The spiked samples were stirred in a vortex and allowed to stand for 15 min before extraction. After that, 10 ml of solution acetonitrile/acetic acid (99:1 v/v) were added and the tube was shaken vigorously by hand for 1 min. After this, 4 g of NaSO4 and 1 g of CH3COONa were added and the tube was stirred in a vortex for 1 min. The extract was centrifuged for 5 min at 4000 rpm. An aliquot of the supernatant (1 ml) was transferred to a 2 ml microcentrifuge tube, containing 150 mg of NaSO4 and 50 mg of PSA, and shaken for 1 min. After this, the extract was again centrifuged for 5 min at 4000 rpm. A 0.5 ml aliquot of the supernatant was transferred to a vial, diluted with 0.5 ml of acetonitrile and injected into the LC-MS/MS system.

Matrix effect-matrix-matched calibration curve (MMCC) To evaluate matrix effects the ratio between chromatographic signals of a matrix-matched standard solution and pure solvent (acetonitrile) standard was calculated. To quantify the presence of a matrix-effect, the chromatographic signals of pesticides in pure acetonitrile were compared with those of pesticides in a mango matrix-matched standard solution; both solutions were prepared at the same concentration of 0.025 mg kg 1. As it is well known that matrix effects occur with many pesticides and different types of matrix, suppression or enhancement are the most common effect. The simplest way to recommended a null matrix effect is the use of MMCC, and that procedure was adopted to quantify mango samples during the validation procedure. Blank matrix final extract (after extraction and clean-up steps) was used to prepare the MMCC by adding to the extract appropriate volumes of working standard solution (200 mg l 1) and acetonitrile to 500 ml of blank mango final extract.

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LC-MS/MS analysis Chromatographic analyses were carried out using an Agilent HPLC system equipped with an automatic degasser and autosampler. The chromatographic separation occurred in a Phenomenex Luna C18 (150 2.0 mm, particle size 5 mm) analytical column. The flow rate was set at 0.3 ml min 1; the temperature of the column was maintained at 30 C. The mobile phases used were as follows. Mobile phase A: water/methanol (95:5 v/v) with 5 mM ammonium formate and 0.1% formic acid. Mobile phase B: water/methanol (5:95 v/v) with 5 mM ammonium formate and 0.1% formic acid. The gradient programme started with 25% of B with a linear increase over 15 min up to 95% of B. This composition was maintained for a further 10 min before returning to the initial composition. The column was re-equilibrated for 8 min at the initial mobile phase. The total run time was 25 min. The injection volume was 5 ml and, to avoid carry over, the autosampler was flushed with a solution of water/acetonitrile (5:95 v/v) for 10 s before sample injection. The HPLC system was connected to a triple quadrupole tandem mass spectrometer API 5000 (Applied Biosystems) with an electrospray (ES) interface operating in positive mode. The following source conditions were used: ion spray voltage, 5500 V; nebuliser gas, 50 psi; curtain gas, 20 psi; heater gas, 55 psi; and ion source temperature, 500 C. Nitrogen gas was used as a desolvation and collision gas. Detection was performed in multiple reaction monitoring (MRM) mode. The detection parameters optimisation was achieved by infusing separately each compound standard solution at a flow rate of 10 ml min 1. Two greatest intensity transitions were selected per analyte. The highest intensity transition was selected for the quantitative purpose and the other for a confirmatory purpose.

Validation parameters The complete validation according to the laboratory’s guideline was performed in mango matrix. The following parameters were evaluated: linearity, selectivity, ion ratio precision, accuracy, precision, limit of quantification (LOQ) and limit of detection (LOD) (Laborato´rio Nacional Agropecua´rio em Goia´s (LANAGRO) 2010). Linearity was tested in the analytical range of 0.01–0.15 mg kg 1. The study of linearity was conducted according to de Souza and Junqueira (2005), in which the ordinary least-square method (OLSM) is the preferential regression model as it is a well-known and very understandable regression model. For this MMCCs were prepared in eight matrix concentration levels (0.01, 0.03, 0.05, 0.07, 0.08, 0.10, 0.12 and 0.15 mg kg 1), in triplicate, by adding

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appropriate volumes of mixed standard solution (200 mg l 1) and acetonitrile to 500 ml of the mango final extract. The assessments of selectivity were based on the analysis of blank mango samples. During the validation procedure, 12 blank mango samples were analysed to prove that there was no interferences peak in the analytes retention time. Ion ratio precision was studied by comparing the ion ratio between the first and second transitions of each compound in 27 spiked samples. Fortified blank samples were used to evaluate accuracy and precision values by adding aliquots of the specific spiking solution to each sample. The samples were spiked at 0.01, 0.05 and 0.10 mg kg 1 in six replicates each, on 3 different days, and the analysis were performed by three different analysts. At the end of the validation experiments it was possible to evaluate the accuracy as recovery values (n ¼ 18), and the precision as intra-day precision and withinlaboratory precision. According to internal quality systems the analytes’ mean recovery (accuracy) must be in the range 70–110%, and the precision values (RSDr and RSDR) must be below 20%. Exceptions can be accepted if the analyte is known to be ‘problematic’. The LOQ was the lowest level for which it has been demonstrated that criteria for accuracy and precision have been met. The LOD was estimated as three times the standard deviation of the precision study obtained at the LOQ.

Assessment of global uncertainty Method uncertainties at the LOQ were evaluated by considering two sources of uncertainty: those associated with the calibration curve and with precision. These two sources showed to be those that most contributed to the total uncertainty of the method.

Results and discussion Extraction procedure As can be seen in the sample extraction procedure, a slight modified buffered QuEChERS was made using anhydrous sodium sulfate instead of anhydrous magnesium sulfate. This modification was proposed due to the availability of laboratory reagents and extraction cost reduction. Pre-validation tests shown good recovery and precision values after the use of anhydrous sodium sulfate. It was then conclude that this modification did not prejudice method performance and both reagents could possibly be used with no significant differences in method performance. The buffered QuEChERS method final extraction aliquot gives a matrix concentration of 1.0 g ml 1. However, during the pre-validation procedure it was observed that some analytes were too sensitive to the

MS/MS detector, showing a too high chromatographic peak area. In consequence, their calibration curves’ linearity range was too low (0.0005–0.050 mg kg 1) and the matrix effect was too high. In order partially to solve these problems, the validated method was designed to give a matrix concentration of 0.5 g ml 1 at the final extract by diluting it with acetonitrile before injection with no harm to low sensitivity analytes.

Liquid chromatography-tandem mass spectrometry During the optimisation of analyte detection it was observed that more than 10% of the analytes generate preferentially ammonium and sodium adducts ([M þ NH4]þ and [M þ Na]þ, respectively) in spite of hydrogen adducts ([M þ H]þ) during the ES-positive ionisation. Also, it was observed that same of these analytes generate very few [M þ H]þ adducts even under acidic pH. The formation of [M þ Na]þ adducts must be avoided due to its low precision at the detector. The presence of ammonium formate buffer suppress the formation of sodium adducts, which are more common under acidic conditions. Therefore, pesticides under ammonium buffer generate predominantly [M þ H]þ and [M þ NH4]þ, which show higher sensitivity (Hiemstra and de Kok 2007) and more reproducible results. However, an acid pH increases the overall sensitivity of a positive-mode ionisation analyte. To deal with that situation, the laboratory developed a mobile phase composition with 0.1% acid formic and 5 mM ammonium formate. That mobile phase composition guarantees an acidic pH and a buffered concentration of ammonium ion in the mobile phase, which increased detection sensitivity and reproducibility (especially for [M þ NH4]þ adducts) and avoided [M þ Na]þ adduct formation as recommended. In total, the ionisation of 98 pesticides in a positivemode electrospray ion source was investigated. The source-dependent parameters (ion spray voltage, nebuliser gas, curtain gas and heater gas pressure, and ion source temperature) were optimised by flow injection analysis (FIA). A very important step during the prevalidation method was MRM optimisation. The laboratory developed a standard procedure consisting of two steps. First, a precursor ion was selected and the declustering potential voltage was optimised for that precursor ion. After that as many product ions as possible were selected and the collision energy voltage was optimised for each ion. At the final stage the two strongest transitions were selected per analyte. The selection criteria were based on the transitions that generate the most intense chromatographic signal with the lowest background noise. Also, to guarantee a high

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Table 1. Chromatographic and MRM transition parameters for pesticides and metabolites. 1st transition TD

Pesticide

Retention time (min)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

Acephate Alachlor Aldicarb Allethrin Azinphos-ethyl Azinphos-methyl Azoxystrobin Bifenthrin Boscalid Bromopropylate Carbaryl Carbendazim Carbofuran Carbophenothion Carbosulfan Chlorfenvinphos Chlorpyrifos Chlorpyrifos methyl Cyfluthrin Cymoxanil Cypermethrin Cyproconazole Deltamethrin Diazinon Dichlorvos Difenoconazole Dimethoate Dissulfoton sulfone Disulfoton Disulfoton sulfoxide Epoxiconazole Ethion Ethoprophos Etrimfos Fenamiphos sulfoxide Fenamiphos Fenamiphos sulf one Fenarimol Fenitrothion Fenpropathrin Fenthion Fenthion sulfone Fenthion sulfoxide Fenvalerate Fipronil Fluazifop butyl Flutriafol Folpet Furathiocarb Imazalil Imidacloprid Iprodione Kresoxim methyl Lambda cyhalothrin Malathion Methamidophos Methidathion Methomyl Mevinphos

1.9 17.0 9.9 19.8 16.7 14.7 15.2 23.8 15.7 19.9 12.5 3.9 20.9 24.4 22.7 17.9 20.1 17.5 21.0 8.6 21.0 16.2/16.6 21.1 18.0 11.6 18.5 7.6 13.7 18.6 13.4 16.9 19.8 16.8 17.8 12.1 17.2 12.5 16.8 13.4 20.6 17.8 13.0 12.4 21.4 17.4 19.3 13.9 16.4 19.4 12.8 6.3 17.1 15.5 20.7 16.0 1.8 14.5 3.6 7.4

Quasi molecular ion [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ

2nd transition

DP (v)

Q1 (amu)

Q2 (amu)

CE (v)

Q1 (amu)

Q2 (amu)

CE (v)

50 40 25 60 50 40 30 40 45 30 55 50 70 50 50 100 70 60 25 35 40 40 38 50 80 80 70 70 25 60 70 40 70 80 120 115 120 80 45 45 80 147 110 50 40 100 80 25 125 50 60 80 80 50 80 50 70 40 40

184.1 270.1 208.1 303.3 346.1 318.0 404.2 440.1 343.1 446.0 202.1 192.1 222.2 343.0 381.3 359.0 350.0 322.0 434.1 199.2 433.1 292.2 523.0 305.1 221.0 406.1 230.0 307.0 275.1 291.1 330.1 385.1 243.1 293.1 320.1 304.1 336.2 331.1 278.1 367.2 279.1 311.1 295.1 437.2 454.1 384.2 302.2 315.1 383.2 297.1 256.1 330.1 314.2 467.2 331.1 142.0 303.1 163.1 225.1

143.1 162.1 116.0 135.1 132.1 132.1 372.2 181.2 307.1 325.0 145.2 160.1 165.2 157.1 118.1 155.1 115.0 143.1 191.1 128.1 191.1 70.0 281.0 169.2 127.0 251.1 199.0 114.9 89.0 185.0 121.1 199.0 131.0 143.1 108.1 217.1 266.0 31.1 143.0 125.1 169.1 11.1 127.0 167.2 368.05 282.1 123.1 163.1 195.1 159.0 175.1 245.0 222.1 225.1 127.0 112.0 85.1 88.0 127.0

15 30 13 19 25 23 24 28 30 28 15 28 20 21 31 19 43 32 17 15 24 35 25 31 27 42 15 42 25 21 37 15 32 41 60 33 30 47 29 26 26 33 45 25 33 30 43 21 28 37 30 25 24 25 19 22 32 18 27

113.0 238.1 89.0 91.0 160.1 160.1 344.1 165.1 271.1 368.9 117.1 132.1 123.1 199.1 160.2 99.0 198.0 125.1 127.0 83.0 127.0 125.0 181.3 153.2 109.0 337.1 143.1 171.1 61.0 213.1 101.0 143.0 114.9 265.1 171.1 234.0 188.1 268.1 125.0 97.2 105.1 127.1 109.1 125.0 290.1 328.1 109.1 130.1 252.1 201.0 209.1 288.1 131.2 141.1 99.0 94.0 145.1 106.0 193.1

35 15 27 57 13 13 35 90 47 30 33 45 35 14 23 47 33 34 38 35 45 45 60 31 33 26 36 19 50 15 73 35 44 25 33 25 40 34 30 48 37 38 52 65 46 26 50 42 20 27 28 20 55 62 35 24 15 19 13

(continued )

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Table 1. Continued. 1st transition TD 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

Pesticide Monocrotophos Myclobutanil Omethoate Oxyfluorfen Paraoxon ethyl Paraoxon methyl Parathion ethyl Penconazole Permethrin Phenthoate Phorate sulfone Phorate sulfoxide Phorate Phosalone Phosmet Pirimicarb Pirimiphos ethyl Pirimiphos methyl Prochloraz Profenofos Prometryn Propamocarb Propargite Propiconazole Pyrazophos Pyridaben Simazine Tebuconazole Terbufos Thiabendazole Thiacloprid Thiamethoxam Thiodicarb Thiophanate methyl Triadimefon Triadimenol Triazophos Trichlorfon Trifluralin

Retention time (min) 4.5 16.3 2.1 19.4 13.6 17.5 15.4 17.8 22.2 17.5 13.7 13.4 18.4 13.4 14.9 10.4 19.6 18.3 18.0 19.2 15.9 2.0 20.36 17.9 18.3 21.4 11.7 17.8 19.6 5.5 9.1 4.0 13.0 11.7 16.2 16.5 16.4 7.4 20.5

Quasi molecular ion [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ

selectivity of the detector, the mass of product ions should be higher than 100 amu and at least 50 amu less than the parent ion, whenever possible. The transition parameters were manually optimised, one by one, to guarantee a high sensitivity and selectivity of the detector for quantification and confirmation. The optimised selected transitions of each compound and their declustering potential (DP) and collision energy (CE) are summarised in Table 1. Each analyte was monitored by two transitions, so the validated method gives a total of 196 MRM transitions. A dwell time of 5 ms was found to be optimum for all compounds. Since the extraction final solvent is 100% acetonitrile and the initial mobile phase composition is a 25% organic solution, solvent incompatibility occurs between the mobile phase and the final

2nd transition

DP (v)

Q1 (amu)

Q2 (amu)

CE (v)

Q1 (amu)

Q2 (amu)

CE (v)

40 60 40 38 70 60 45 60 40 80 70 50 50 125 80 50 80 70 40 115 50 60 30 70 30 50 80 60 40 60 70 60 60 60 60 50 50 100 60

224.1 289.2 214.1 379.1 276.2 292.1 264.1 284.1 408.2 321.1 293.1 277.1 261.0 368.0 318.0 239.2 334.2 306.1 376.15 373.0 242.2 189.2 368.2 342.1 374.1 365.2 202.0 308.2 289.1 202.1 253.1 292.1 355.1 343.05 294.15 296.2 314.2 257.0 336.2

127.0 70.1 183.05 316.0 220.0 236.0 143.1 70.1 183.1 247.0 114.9 143.1 75.0 182.1 160.1 72.1 198.1 108.1 266.0 302.9 158.15 102.0 231.2 159.0 222.1 147.2 104.0 70.1 103.0 175.1 126.0 211.1 88.0 151.1 197.15 70.1 162.1 127.0 252.0

25 35 17 26 24 25 26 29 28 17 42 30 20 25 28 39 33 44 25 28 34 27 17 45 32 38 38 55 16 37 34 20 28 32 23 26 27 27 27

193.1 125.0 127.0 237.0 94.1 254.0 125.0 159.0 153.1 135.1 171.1 115.0 199.0 75.1 133.1 182.25 182.25 164.15 308.0 144.1 200.1 74.1 175.2 69.1 194.2 132.2 132.1 125.1 57.05 131.2 186.1 181.2 108.0 311.1 69.05 99.1 119.1 221.0 236.1

14 43 39 40 53 23 32 42 65 30 18 45 13 97 53 25 34 32 19 53 27 38 25 31 48 65 28 57.3 35 47 21 33 24 18 32 23 50 17 24

extract solvent, particularly for the early eluting compounds. However, by analysing the overall results it was clear that this low organic initial mobile phase linearity, ramped to a high organic mobile phase, was necessary to achieve a good resolution and distribution of the peaks during the chromatogram (Figure 1). High polar analytes such as acephate, methamidophos, methomyl, omethoate and propamocarb elute at the beginning of the chromatogram (retention times ¼ 1.9, 1.8, 3.6, 2.1 and 2.0 min, respectively), and because of that high polarity solvent incompatibility occurs (Figures 1 and 2). These analytes generate bad shape, flat top, tailed and split peaks. However, these facts did not prejudice the validation results because of the high sensitivity/specificity of the detector.

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Figure 1. Extracted ion chromatograms (XICs) corresponding to 98 pesticides (only the quantification transition) in mango matrix spiked at 0.10 mg kg 1.

Linearity and matrix effect Pre-validation tests demonstrated that the matrix effect was present for a large number of analytes. For example, pesticides such as azoxystrobin, folpet, procymidone, prochloraz and simazine showed enhancement above 10%, while acephate, aldicarb, carbosulfan fenemiphos sulfon, phorate sulfoxide, imazalil, imidacloprid, methamidophos and omethoate showed suppression above 15%. Some pesticides such as alachlor, azinphos-ethyl, carbophenothion, cyproconazol, oxyfluorfen, penconazole and permethrin did not show any matrix effects. In view of this variability, it was decided that the simplest solution to deal with these matrix effects was the use of MMCC for quantification purposes. To fit a calibration function by OLSM, several assumptions related to the residuals (normality, homoscedasticity and independency) and to the model are required. A consequence of the indiscriminate use of the OLSM

is the omission of the assumptions tests and frequently this is an important source of errors in analytical chemistry. The MMCCs studied were approved in all statistical tests carried out with the initial data. The outliers were identified by a Jackknife residual test and excluded within the maximum percentage permitted (22% of the original data). The residuals followed a normal distribution based on the Ryan–Joiner test. In addition, the Durbin–Watson test revealed that there was no positive residual autocorrelation, which means that the residuals were independent. The homoscedasticity of the residuals was proven by a Brown–Forsythe test. Furthermore, the linear regression shown to be significant and the regression coefficients were above 0.98 for almost all analytes. As a result, the OLSM was shown to be a good assumption for the MMCC for all analytes in the specified range (Table 2, linearity), with the exception of triazophos which showed a quadratic function as a

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Figure 2. Extracted ion chromatograms (XICs) of mango matrix spiked at 0.01 mg kg 1 showing the bad peak shape of (a) acephate, (b) methamindophos, (c) propamocarb, (d) omethoat, (e) methomyl and (f) carbendazim.

better fitting model. The values obtained during the linearity assessment are omitted from this paper. As can be seen in Table 2, all pesticides showed a linear range from 0.01 to 0.15 mg kg 1, the exceptions being flutriafol, phorate, pirimiphos ethyl, pirimiphos methyl, prometryne and pyridabem, which exhibited a narrowed linear range, probably as a consequence of the high sensitivity of the detector for these compounds. Probably another regression assumption should be more appropriate if a range greater than 0.08 mg kg 1 is used. Specificity The method was shown to be very specific since all 12 blank mango matrix samples analysed did not show any interference peak at the analyte retention time. This high specificity is expected to be found for a triple quadrupole (TQ) mass spectrometry detector operating

on MRM mode, in which even co-eluting analytes do not interfere with each other. Despite the high specificity achieved with a TQ system, some matrix interfering peaks were found. Figure 3 shows a blank mango sample analysed in MRM mode. As can be seen there is matrix interference at 1.2, 11.4, 16.7 and 17.2 min and some in the range 19â&#x20AC;&#x201C;24 min. Fortunately, none of those interfering peaks co-eluted with any pesticides (Figure 4). Figure 4 shows an example of four pesticide chromatograms in which the interfering peak was found, demonstrating that those interfering peaks are fully resolved from the analytesâ&#x20AC;&#x2122; peaks.

Ion ratio precision The mean, standard deviation (SD) and relative standard deviation (RSD) of the ionsâ&#x20AC;&#x2122; ratio were calculated.

Pesticide

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

0.003 0.002 0.002 0.003 0.004 0.003 0.001 0.001 0.004 0.005 0.004 0.003 0.004 0.002 0.002 0.002 0.004 0.006 0.004 0.009 0.005 0.002 0.003 0.002 0.003 0.002 0.004 0.003 0.003 0.003 0.003 0.002 0.003 0.003 0.004 0.002 0.004 0.004 0.003

LOD (mg kg 1) 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

LOQ (mg kg 1) 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.12 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15

Range (mg kg 1) R2 0.9946 0.9994 0.997 0.9924 0.9933 0.9922 0.9986 0.9978 0.9869 0.9852 0.9963 0.9946 0.9901 0.9992 0.9973 0.9993 0.9931 0.9955 0.994 0.9848 0.9853 0.9903 0.9988 0.993 0.9994 0.9911 0.99 0.994 0.984 0.9951 0.9899 0.9903 0.9979 0.9991 0.9954 0.9966 0.9891 0.9956 0.9979

Linearity

87 100 92 102 102 96 97 88 105 91 94 87 99 92 85 93 99 103 101 86 89 91 96 94 98 95 104 96 101 99 95 95 99 96 97 96 86 96 88

Mean recovery (%) 7 4 8 9 9 11 5 4 10 13 14 9 12 4 6 5 8 7 11 14 17 4 7 7 7 7 12 10 10 7 6 5 5 4 15 4 5 13 13

RSDr (%)

0.01 mg kg 1

7 6 8 11 9 12 5 5 10 13 15 11 14 5 10 8 16 8 15 15 18 4 8 7 8 8 14 10 10 12 8 5 10 7 15 4 9 13 13

RSDR (%) 78 98 98 105 106 105 106 97 102 103 94 97 109 102 91 99 101 101 98 96 102 100 100 101 101 103 101 101 97 103 104 102 105 102 101 102 107 92 97

Mean recovery (%) 9 9 8 8 7 9 4 3 13 8 7 5 10 5 4 6 4 8 6 6 14 3 4 5 5 5 5 5 13 10 5 3 5 9 13 4 2 5 8

RSDr (%)

0.05mg kg 1

Table 2. Validation parameters, LOD, LOQ, linearity, accuracy, precision and the method uncertainty at the LOQ.

11 9 9 9 9 10 6 4 13 8 7 5 10 5 8 7 5 11 6 8 14 3 7 6 5 11 5 5 13 10 5 3 7 9 13 4 2 5 8

RSDR (%) 71 100 90 105 97 100 101 93 95 102 91 95 98 99 86 100 101 99 104 102 101 99 94 100 102 95 99 98 92 95 100 101 102 101 99 101 95 96 101

Mean recovery (%) 7 4 5 7 11 4 5 4 9 5 9 7 6 5 6 7 3 7 2 6 17 4 4 1 4 9 3 6 13 11 9 7 4 6 12 3 4 7 5

RSDr (%)

0.1mg kg 1

7 4 12 10 12 8 5 5 9 5 10 12 7 6 8 7 4 9 2 6 18 4 5 2 4 10 4 6 15 11 9 8 4 7 13 3 5 7 5

RSDR (%)

(continued )

22 14 20 30 27 30 14 15 38 38 36 28 36 12 23 19 39 21 35 32 46 23 19 23 18 27 36 28 33 30 28 26 23 16 34 16 30 32 31

Expanded uncertainty (%)

Food Additives and Contaminants 649

Pesticide

Fenpropathrin Fenthion Fenthion sulfone Fenthion sulfoxide Fenvalerate Fipronil Fluazifop butyl Flutriafol Folpet Furathiocarb Imazalil Imidacloprid Iprodione Kresoxim methyl Lambda cyhalothrin Malathion Methamidophos Methidathion Methomyl Mevinphos Monocrotophos Myclobutanil Omethoate Oxyfluorfen Paraoxon ethyl

40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

Table 2. Continued.

0.002 0.004 0.005 0.004 0.003 0.004 0.003 0.004 0.004 0.003 0.003 0.003 0.004 0.003 0.004 0.004 0.004 0.004 0.003 0.004 0.003 0.002 0.002 0.013 0.004

LOD (mg kg 1) 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.01

LOQ (mg kg 1) 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.12 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15

Range (mg kg 1) R2 0.9918 0.9963 0.9928 0.9981 0.9982 0.9898 0.9954 0.9875 0.9969 0.9842 0.9954 0.9855 0.988 0.9988 0.9933 0.9928 0.9802 0.9939 0.9953 0.99 0.9973 0.9988 0.9975 0.9938 0.9955

Linearity

100 110 103 100 99 85 102 92 98 87 96 99 94 106 93 103 72 97 106 95 89 100 83 88 89

Mean recovery (%) 6 8 12 9 8 17 9 6 10 8 4 12 9 5 11 8 14 14 10 10 5 6 5 21 9

RSDr (%)

0.01 mg kg 1

6 8 12 13 10 17 12 17 17 8 12 12 10 5 18 9 19 14 10 14 6 6 8 22 9

RSDR (%) 103 103 104 103 103 101 100 108 94 100 107 101 88 101 102 107 76 98 103 102 94 104 81 102 106

Mean recovery (%) 6 11 11 8 7 6 6 6 9 9 5 8 6 5 6 11 14 5 12 7 4 9 8 3 13

RSDr (%)

0.05mg kg 1

7 11 16 8 9 7 6 8 10 9 5 9 7 5 6 13 18 6 13 11 9 9 9 3 14

RSDR (%)

99 96 90 98 103 97 97 97 93 96 102 98 91 101 98 101 66 99 99 91 90 100 79 99 94

Mean recovery (%)

4 9 17 5 3 5 9 4 9 9 6 12 12 5 4 12 9 8 7 13 8 7 7 6 4

RSDr (%)

0.1mg kg 1

4 10 17 5 4 5 10 5 9 9 6 12 12 5 4 12 9 9 7 15 9 7 8 7 4

RSDR (%)

24 21 37 30 23 43 29 41 38 34 30 37 33 13 42 27 53 35 26 36 17 16 22 23 25

Expanded uncertainty (%)

650 N. Fleury Filho et al.

65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

Paraoxon methyl Parathion ethyl Penconazole Permethrin Phenthoate Phorate sulfone Phorate sulfoxide Phorate Phosalone Phosmet Pirimicarb Pirimiphos ethyl Pirimiphos methyl Prochloraz Profenofos Prometryn Propamocarb Propargite Propiconazole Pyrazophos Pyridaben Simazine Tebuconazole Terbufos Thiabendazole Thiacloprid Thiamethoxam Thiodicarb Thiophanate methyl Triadimefon Triadimenol Triazophos Trichlorfon Trifluralin

0.030 0.004 0.002 0.002 0.005 0.003 0.002 0.003 0.004 0.003 0.004 0.003 0.002 0.002 0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.007 0.002 0.002 0.003 0.004 0.002 0.003 0.005 0.003 0.002 0.003 0.004 0.018

0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05

0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.12 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.08 0.01–0.08 0.01–0.15 0.01–0.15 0.01–0.08 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.12 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.15 0.01–0.10 0.01–0.15 0.01–0.15

0.9972 0.9965 0.9741 0.9917 0.9928 0.9984 0.9964 0.9888 0.9948 0.996 0.9736 0.996 0.9975 0.9974 0.9962 0.9944 0.9938 0.9987 0.9987 0.9988 0.9962 0.9806 0.9931 0.9987 0.9968 0.9847 0.9928 0.9981 0.9892 0.994 0.997 0.9797 0.9948 0.9663

97 102 94 90 93 95 99 91 94 93 98 92 91 97 101 87 72 99 100 94 90 101 99 100 96 89 94 95 98 100 95 94 99 116

15 13 7 7 18 4 8 6 11 5 14 5 6 6 8 8 6 4 6 7 4 11 5 5 7 14 7 10 13 6 6 6 12 55

21 13 9 7 18 7 8 6 14 8 14 14 10 9 9 9 12 9 8 8 9 19 6 6 10 14 8 12 17 6 9 7 12 57

98 107 104 97 103 101 107 100 108 102 102 107 105 98 103 103 70 101 104 102 103 94 103 104 101 101 88 104 104 103 101 103 98 106

15 5 2 5 14 6 6 8 4 8 13 1 4 3 7 7 10 3 4 6 4 12 4 4 8 11 6 9 12 7 7 9 9 12

15 5 2 5 14 6 7 8 5 8 13 1 4 5 7 7 11 3 4 6 5 17 5 5 10 13 9 11 16 9 7 9 12 12

102 101 97 95 101 99 96 102 96 99 95 95 100 98 101 94 68 101 99 99 99 91 100 100 93 92 96 93 96 95 98 99 97 96

14 8 2 3 19 4 8 4 8 10 7 2 4 4 2 3 6 2 5 3 3 12 3 3 9 12 9 14 13 7 4 7 12 15

14 8 4 3 19 5 8 7 8 13 9 3 5 4 2 7 6 2 5 3 4 15 4 4 9 12 9 14 13 9 5 7 18 15

47 31 45 24 43 16 21 23 35 22 38 31 21 22 23 23 31 21 18 19 22 55 23 16 24 41 25 27 43 21 22 30 30 41

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Figure 3. Extracted ion chromatogram (XICs) of all 98 pesticides (only the quantification transition) corresponding to the analysis of organic blank mango.

According to SANCO (2010) there is a maximum permitted tolerance for relative ion intensities’ variation using a range of spectrometric techniques to confirm the presence of any residue detected. All RSDs of the ions ratio comply with the maximum permitted tolerances. As can be seen in Table 3, almost all analytes showed an ion ratio RSD below 20%. Only folpet and phorate give RSDs of 25.97% and 26.01%, respectively. That higher value was accepted since the ion ratios for these analytes are lower than 10%. In that case the maximum permitted tolerance is 50%. Cyfluthryn did not show any amenable confirmatory transition. By using two MRM transitions the analytical method achieves a total of four identifications points, minimising false-positive results. From these results, it is safe to say that the method has adequate identification points to confirm the presence of 97 analytes.

Accuracy and precision Method accuracy was estimated as mean recovery (n ¼ 18). Method precision was calculated as intra-day precision (repeatability, RSDr) and within-laboratory precision (reproducibility, RSDR) of the method. As can be seen from Table 2, almost 95% of the analytes shown mean recoveries between 80% and

110% at all three levels. Only methamidophos and propamocarb at 0.10 mg kg 1 showed mean recoveries below 70%, 66% and 68%, respectively. This low recovery is probably due to the high polarity of these molecules, as can be expected since both molecules have amino groups that generate a high intermolecular bonding (hydrogen bounding), therefore during the liquid extraction a fraction of theses analytes may still remain in the aqueous phases. The precision study demonstrated that all analytes showed good precision (RSDr and RSDR). Only oxyfluorfen, paraoxon methyl and trifluralin showed RSDR values above 20% at 0.01 mg kg 1. Probably the high RSD of these analytes is a consequence of their low sensitivity in the ES-MS/MS detector. Despite of the low recovery values of methamidophos and propamocarb, their precision values (RSDr and RSDR) were good enough to accept their validation results and to analyse them in routine. In general, the validation data for all pesticides are in accordance with European Union guidelines for pesticide residue analysis (SANCO 2010) and the Brazilian validation guide (Instruc¸a˜o Normativa No. 24, 2009). Based on these results, it was demonstrated that the lowest spiking level (0.01 mg kg 1) was determined as the LOQ for all analytes, with the exceptions of oxyfluorfen, paraoxon methyl and trifluralin, which

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Figure 4. Extracted ion chromatograms (XICs) of blank mango (left) and fortified mango at 0.01 mg kg 1 (right) of (a) fenarimol, (b) malation, (c) miclobutanil and (d) paraoxon methyl, showing that the analyte peaks (filled peaks) do not co-elute with matrix interferences (outlined peaks).

Analyte

8 108 50 81 64 253 50 49 41 76 23 17 86 6 54 98 119 119 – 44 23 32 10 70 37 13 31 42 33 67 26 83 48 3 1 4 6 1 5 5 4 8 2 6 3

7 1 2 8 9 13 – 7

1 6 5 6 6 21 5 3 5 14 3

13 5 11 7 9 8 10 5 12 18 11 11 9 8 3 8 7 11 – 16 10 9 10 5 15 5 16 11 12 11 8 7 7

Mean (%) n ¼ 27 SD RSD (%)

Ion ratio

Etrimfos Fenamiphos sulfoxide Fenamiphos Fenamiphos sulfone Fenarimol Fenitrothion Fenpropathrin Fenthion Fenthion sulfone Fenthion sulfoxide Fenvalerate Fipronil Fluazifop butyl Flutriafol Folpet Furathiocarb Imazalil Imidacloprid Iprodione Kresoxim methyl Lambda cyhalothrin Malathion Methamidophos Methidathion Methomyl Mevinphos Monocrotophos Myclobutanil Omethoate Oxyfluorfen Paraoxon ethyl Paraoxon methyl Parathion ethyl

Analyte

Ion ratio

47 34 65 80 96 77 9

122 71 36 78 109 20 12 76 87 62 72 24 44 46 1 43 49 106 8 87 76 90 44 143 51

7 10 4 13 10 3 1 6 16 9 11 3 4 6 0 3 5 16 1 8 12 16 5 14 9 3 7 3 7 10 13 13 2

6 15 11 17 9 13 10 7 18 15 16 13 8 12 26 7 10 15 15 10 16 18 11 10 17 14 14 7 11 13 14 17 19

Mean (%) n ¼ 27 SD RSD (%)

Table 3. Precision of ion ratio values for all 98 analytes in 27 fortified mango samples.

Penconazole Permethrin Phenthoate Phorate sulfone Phorate sulfoxide Phorate Phosalone Phosmet Pirimicarb Pirimiphos ethyl Pirimiphos methyl Prochloraz Profenofos Prometryn Propamocarb Propargite Propiconazole Pyrazophos Pyridaben Simazine Tebuconazole Terbufos Thiabendazole Thiacloprid Thiamethoxam Thiodicarb Thiophanate methyl Triadimefon Triadimenol Triazophos Trichlorfon Trifluralin

Analyte 53 12 117 74 77 4 46 22 27 48 81 321 22 59 39 78 78 79 27 72 10 110 70 6 47 40 9 151 7 43 29 1

Mean (%) n ¼ 27

3 1 7 7 8 1 4 3 3 5 4 22 1 6 7 3 4 4 1 8 1 7 8 1 7 5 0 13 1 6 4 0.1

Ion ratio

5 7 6 9 11 26 9 13 12 10 5 7 5 10 17 4 6 6 5 11 7 7 11 14 15 13 18 9 19 14 13 9

RSD (%)

654 N. Fleury Filho et al.

Food Additives and Contaminants had only the spiking level of 0.05 mg kg 1, to meet the minimal requirements of precision.

Measurement of uncertainty Total uncertainty was evaluated assuming that both contributions (regression standard deviation and within-laboratory precision) were independent of each other. The regression standard deviation was used to estimate the uncertainty associated with the calibration curve; the within-laboratory precision was used to estimate the uncertainty associated with the method precision. The uncertainty associated with the calibration curve was calculated according to Miller and Miller (2005); the uncertainty associated with the within-laboratory precision was estimated by the standard deviation at the method LOQ. Each individual source of uncertainty was estimated and then combined and expanded by a t-distribution coverage factor (k) at a confidence level of 95.45%. Table 2 shows the uncertainty results for all analytes; the results are expressed as the percentage at the LOQ. By analysing the data, it was possible to recognize that the uncertainty associated with the within-laboratory reproducibility is that which contributes most to the global uncertainty of the method. Analytes with an expanded uncertainty value above 40% are also a consequence of high uncertainty associated with the calibration curve.

Conclusion The QuEChERS method was adapted and validated for the determination of 98 pesticides and metabolites in mango. The final result demonstrated that the method is suitable for application to pesticide residue monitoring/enforcement programmes. Validation results showed the OLSM to be a good assumption for the calibration curve for 97 analytes; also the method showed excellent recoveries and precision for all analytes studied meeting Brazilian and European Union guidelines for method performance criteria. The validation procedure employed guarantees high analytical reliability and that the laboratory can apply the method in routine analysis with minimal false-positive results.

Acknowledgements The authors are thankful to the National Counseling for Technological Research Development (CNPq) for scholarships support. Thanks are also extended to the Central Science Laboratory (CSL-UK) and the Community Reference Laboratory for Residues of Pesticides in Fruits and Vegetables in Spain for technical support.

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References Anastassiades M. 2006. A mini multi-residue method for the analyses of pesticide residues in low fat products [internet]; [cited 2010 July 20]. Stuttgart (Germany): CVUA. Available from: http://www.quechers.com Anastassiades M, Lehotay SJ, Stajnbaher D, Schenk FJ. 2003. Fast and easy multiresidue method employing acetonitrile extraction/partition and ‘dispersive solid phase extraction’ for the determination of pesticide residue in produce. J AOAC Int. 86:412–431. Banerjee K, Oulkar DP, Dasgupta S, Patil SB, Patil SH, Savant R, Adsule PG. 2007. Validation and uncertainty analysis of a multi-residue method for pesticides in grapes using ethyl acetate extraction and liquid chromatographytandem mass spectrometry. J Chromatogr A. 1173:98–109. De Souza SVC, Junqueira RG. 2005. A procedure to assess linearity by ordinary least squares method. Analyt Chim Acta. 552:25–35. Dı´ ez C, Traag WA, Zomer P, Marinero P, Atienza J. 2006. Comparison of an acetonitrile extraction/partitioning and ‘dispersive solid-phase extraction’ method with classical multi-residue methods for the extraction of herbicide residue in barley samples. J Chromatogr A. 1131:11–23. Eurachem. 1998. The fitness for purpose of analytical methods. A laboratory guide to method validation and related topics. Teddington, UK: Eurachem. European Commission. 2002. Commission Decision No. 657/2002 of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off J Eur Comm. L. 221:8–36. Ferrer I, Thurman EM. 2007. Multi-residue method for the analysis of 101 pesticides and their degradates in food and water samples by liquid chromatography/time-of-flight mass spectrometry. J Chromatogr A. 1175:24–37. Gilbert-Lo´pez B, Garcı´ a-Reyes JF, Fernańdez-Alba AR, Molina-Dı´ az AM. 2010. Evaluation of two sample treatment methodologies for large-scale pesticide residue analysis in olive oil by fast liquid chromatography-electrospray mass spectrometry. J Chromatogr A. 1217:3736–3747. Hiemstra M, de Kok A. 2007. Comprehensive multi-residue method for the target analysis of pesticides in crops using liquid chromatography-tandem mass spectrometry. J Chromatogr A. 1154:3–25. Instituto Nacional de Metrologia, Normalizaçaõ e Qualidade Industrial (INMETRO). 2007. DOQ-CGCRE-008. Orientaço˜es sobre validaçaõ de me´todos de ensaio quı´ micos. Instruçaõ Normativa No. 24, de 14 de julho de 2009. Define os requisitos e crite´rios especı´ ficos para funcionamento dos Laborato´rios de Ana´lises de Resı´ duos e Contaminantes em Alimentos integrantes da Rede Nacional de Laborato´rios Agropecua´rios. Dia´rio Oficial da Uniaõ de 22/07/2009, Seçaõ 1. p. 7. Instruçaõ Normativa No. 26, de 08 de outubro de 2010. Definir, para as culturas agrı´ colas de abacaxi, alface, alho, amendoim, arroz, banana, batata, cafe´, castanha-dobrasil, feijaõ, laranja, limaõ, lima aćida, maça˜, mamaõ, manga, melaõ, milho, morango, pimenta-do-reino, pimentaõ, soja, tomate, trigo e uva, no ano safra 2010/2011, os limites ma´ximos de resı´ duos e de contaminantes tolerados

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para fins de monitoramentos de agroto´xicos, bem como os tipos de ana´lises e nu´mero de amostras a serem coletados. Dia´rio Oficial da Uniaõ de 14/10/2010, Seçaõ 1. p. 6. Koesukwiwat U, Lehotay SJ, Miao S, Leepipatpiboom N. 2010. High throughput analysis of 150 pesticides in fruits and vegetables using QuEChERS and low-pressure gas chromatography-time-of-flight mass spectrometry. J Chromatogr A. 1217:6692–6703. Laborato´rio Nacional Agropecua´rio em Goia´s (LANAGRO). 2010. IT UGQ 005 – Validaçaõ intralaboratorial de me´todos de ensaio. Goiaˆnia: LANAGRO. Lehotay SJ, de Kok A, Hiemstra M, van-Bodegraven P. 2005. Validation of a fast and easy method for the determination of residues from 229 pesticides in fruit and vegetables using gas and liquid chromatography and mass spectrometry detection. J AOAC Int. 88:595–614. Lehotay SJ, Mastovska´ K, Ligthfield AR. 2005. Use of buffering and other means to improve results of problematic pesticides in a fast and easy method for residue analysis of fruits and vegetables. J AOAC Int. 88:615–629. Lehotay SJ, Son KA, Kwon H, Koesukwiwat U, Fu W, Mastovska K, Hoh E, Leepipatboon N. 2010. Comparison of QuEChERS sample preparation methods for the analysis of pesticide residues in fruits and vegetables. J Chromatogr A. 1217:2548–2560. Maurı´ cio AQ, Lins ES, Alvarenga MB. 2009. A national residue control plan from the analytical perspective – the Brazilian case. Analit Chim Acta. 637:333–336. Miller JN, Miller JC. 2005. Statistics and chemometrics for analytical chemistry. 5th ed. UK: Pearson Education Limited. Ministe´rio da Agricultura, Pecua´ria e Abastecimento (MAPA). 2009. Intercaˆmbio Comercial do Agronegoćio: Principais mercados de destino. Brası´ lia (Brazil): MAPA/ACS.

Ministe´rio da Agricultura, Pecua´ria e Abastecimento (MAPA). 2010. A Forc¸a da Agricultura: 1860–2010. Brası´ lia (Brazil): MAPA/ACS. Moreno JLF, Frenich AG, Bolano˜s PP, Vidal JLM. 2008. Multiresidue method for the analysis of more than 140 pesticide residues in fruits and vegetables by gas chromatography coupled to triple quadrupole mass spectrometry. J Mass Spectrom. 43:1235–1254. Pan J, Xia XX, Liang J. 2008. Analysis of pesticide multiresidues in Leafy vegetables by ultrasonic solvent extraction in liquid chromatography-tandem mass spectrometry. Ultrasonic Sonochem. 15:25–32. Revista Online Brasil Alimentos. 2009. Frutas – Brasil e´ o terceiro maior produtor de frutas [internet]. Revista Online Brasil Alimentos, 5 August; [cited 2010 July 20]. Available from: http://www.brasilalimentos.com.br/ neg%C3%B3cios/2009/brasil-%C3%A9-o-terceiro-maiorprodutor-mundial-de-frutas/ SANCO. 2010. Document No. SANCO/10684/2009. Method validation and quality control procedures for pesticide residues analysis in food and feed. AL-11332009:1–40. Shimelis O, Yang Y, Stenerson K, Kaneko T, Ye M. 2007. Evaluation of solid-phase extraction dual-layer carbon/ primary secondary amine for clean up of fatty acid matrix components from food extract in multiresidue pesticide analysis. J Chromatogr A. 1165:18–25. Thompson M, Ellison SLR, Wood R. 2002. Harmonized guidelines for single laboratory validation of methods of analysis. Pure Appl Chem. 74:835–855. Wang S, Xu Y, Pan C, Jiang S, Liu F. 2007. Application of matrix solid-phase dispersion in liquid chromatography-mass spectrometry to fungicide residue analysis in fruit and vegetables. Anal Bioanal Chem. 378:673–685.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 657–664

A multi-residue method for the determination of pesticides in high water content matrices by gas chromatography–single quadrupole mass spectrometry with electron ionisation (EI-GC/MS) Mauro Lućio Gonçalves de Oliveira*, Fernando Diniz Madureira, Fabiano Aure´lio, Ana Paula Pontelo, Gilsara Silva, Reginaldo Oliveira and Claúdia Paes Laborato´rio Nacional Agropecua´rio – LANAGRO/MG, Ministry of Agriculture, Livestock and Food Supply of Brazil, Pesticides Laboratory, Av. Roˆmulo Joviano s/n – Fazenda Modelo, Postal Code: 33.600-000, Pedro Leopoldo-MG/Brazil (Received 26 November 2010; final version received 7 November 2011) An EI-GC/MS method for the determination of pesticide residues in vegetable matrices with high water content was validated using papaya samples. The validation of a multi-residue pesticide method was in agreement with national and international regulations enabling the Ministry of the Agriculture, Livestock and Food Supply of Brazil to cover a large number of matrices and pesticide residues in its monitoring and control programmes. The extraction used 60 mL of ethyl acetate and 30 g of sample previously processed. After extraction, clean-up of all the extracts was carried out by percolation through GBC cartridges. The samples were then injected in an EI-GC/MS system. Calibration curves were prepared in quadruplet by fortifying blank extracts with a standard solution containing all the pesticides studied at 0.000, 0.005, 0.010, 0.020, 0.030, 0.050, 0.080 and 0.100 mg kg 1. For the recovery study, blank samples were fortified at 0.010, 0.020, 0.030, 0.050 and 0.080 mg kg 1 and then submitted to the extraction procedure. The complete procedure was repeated over four different days by two analysts. The regression parameters of calibration curves were calculated for each validation day. Linearity, selectivity, specificity, robustness, limits of detection and quantification were also assessed. The uncertainty was estimated for each analyte at each spike level studied. The method had recoveries between 91% and 105% and precision results 20%. Limits of quantification were below or equal to the maximum residue limits (MRLs) regulated by Brazilian legislation. The MRLs of the selected pesticides are not regulated by CODEX Alimentarius. The results are also in agreement with SANCO/10684/2009. Keywords: chromatography–GC; pesticide residues; vegetables

Introduction The Ministry of Agriculture, Livestock and Food Supply of Brazil is co-responsible for the control of pesticide residues registered for different applications (BRAZIL 2009). The challenge of including as many analytes as possible in the same chromatographic run arises in laboratories all over the world. It would not be viable to analyse a great number of pesticides in different ways, because of the short storage period of some vegetables and to meet the rising world demand for food. In addition, the concept of multi-residue methods capable of analysing varied classes of substances at very low concentrations necessitates equipment that has high sensitivity and selectivity (Pang et al. 2006). The extraction method is also critical because it must extract compounds with different chemical characteristics, the method used cannot be very complicated and it must afford satisfactory recoveries of all analytes. Nowadays simple quadrupole GC/MS systems are commonly found in residue laboratories, universities

and research centres. Indeed they cost less than half the price of a GC or LC coupled to triple-quadrupole mass spectrometer, which are still very expensive and have high maintenance costs. In this sense, the validation of multi-residue methods for analysis of pesticides by EI-GC/MS systems enables laboratories to undertake studies with higher efficiency for a larger number of pesticides with low polarity and with smaller molecules that are typically better separated by GC. EI-GC/MS methods enable pesticide research in different countries all over the world. The method validation design for analysis of pesticide residues should follow some guidelines that indicate how the work should be conducted, the number of samples to be analysed in each type of study and the performance and good laboratory practice. The European Commission, through the Directorate General Health and Consumer Protection (DG SANCO), has promulgated such recommendations for the control of analytical quality and validation of methods for analysis of pesticides residues in its document SANCO/10684/2009. It is also available

*Corresponding author. Email: mauro.oliveira@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.642102 http://www.tandfonline.com

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in guideline format from CODEX Alimentarius and concerns good laboratory practice for the analysis of residues which draws the parameters to be assessed in the validation of methods. It has also been a tendency among the reference laboratories in that area to adopt these recommendations in their validation works in addition to the certification of all the methods, procedures and documents according to the standard ISO/IEC 17025 (ABNT 2005). To guarantee that the analytical methods developed by the laboratory are technically adequate for the generation of analytical results, it is a requirement of ISO/IEC 17025 that every procedure is validated. The validation of a quantitative method for multiresidue analysis of pesticides in vegetable matrices by GC/MS (single quadrupole) requires several studies to be conducted and tests to verify compliance with national and international requirements such as selectivity, linearity, robustness, homoscedasticity of the instrumental response, recovery, precision, limits of detection and quantification of the method (Thompson et al. 2000; IPAC 2005, 2007; INMETRO 2010a). The method validation undertaken was designed in agreement with the recommendations and appropriate normatives and was executed in order to supply enough data for the estimation of measurement uncertainty in a manner as realistic as possible.

Materials and methods Chemicals and reagents The preparation of the mix of pesticides standards (4 ng mL 1 for each pesticide) was carried out at the Pesticides Laboratory of LANAGRO/MG. All the analytical standards used were produced by SigmaAldrich. For the clean-up stage, the cartridges used were Supelclean ENVI-Carb SPE (0.5 g and 6 mL; ref., fill SP4910A). Ethyl acetate used in the extraction and clean-up of the samples was from Burdick and Jackson (ref., fill CR484). Glass flasks (250-mL) with cover (Scott, ref.), 50-mL conical-bottom centrifuge tubes and 2-mL polyethylene microcentrifuge tubes were used for extraction and clean-up of samples. A triphenyl phosphate solution (TPP) (Sigma-Aldrich) at 10 ng L 1 was used as internal standard.

Equipment IKA-Werke’s Ultra-Turrax model T25 Basic (IKAWerke) at 24,000 rpm, CentriVap concentrator systems (Labconco) and vortex mixer MS3 (IKA) at 3000 rpm were used to concentrate and prepare the extracts. A balance (BEL Engineering, 0.001 to 500 g) was used to weigh the samples, and a balance model AVW220D (Shimadzu, 0.00001 to 220 g) was used to prepare stock

standards. For volume measurements, Dispensette (BRAND) and micropipettes (LINEAR and Nichipet EX) were used. A FOCUS GC (Thermo Scientific) gas chromatograph system with autosampler model AS 3000 was used in this work. Coupled to GC system, a single quadrupole mass spectrometer (DSQ series, Thermo Scientific, USA) was used in the electron-impact (EI) ionisation mode (electron energy 70 eV) and source temperature of 230 C. The transfer line was maintained at 290 C. A 30 m 0.25 mm i.d. column (SGE BPX5) was used, with 0.25-mm film, 5% phenyl polysilphenylene-siloxane stationary phase. The capillary injector worked in splitless mode and 2 mL was injected. Helium was used as carrier gas at constant flow (1 mL min 1). The column flow was set at 1.2 mL min 1 and inlet temperature was set at 290 C. The oven temperature ramp was set as 50 C (1 min) at 30 C min 1, 130 C (1 min) at 5 C min 1, 250 C at 5 C min 1, to 300 C (2 min). Some parameters such as ion relative intensity, retention time, estimated time between two searches of a particular fragment (scan time) and the estimated time of selection of a fragment (dwell time) are MS system parameters and also must be studied for method validation. The scan time and dwell time set are presented in Table 3. All these parameters comprise a group of requirements to be assessed before the validation process as performance tests and configuration of the most appropriate instrumental conditions (WHO 2003, 2005; SANCO 2009; INMETRO 2010a). In the case of the method validated, the parameters of the GC/MS system were configured after full scan injections of pure standards and mixtures of the validated analytes. The software used for integration and acquisition of data was Xcalibur (Thermo Electron Corporation), version 1.4 SR1. The data treatment was carried out in an Excel spreadsheet developed inside the laboratory for validation purposes.

Sample preparation The matrix used in that validation process was obtained from local producers, whose cultivation was organic and was determined to be free of pesticide residues. Comminuted papaya samples were weighed (30 g) in 250-mL glass flasks (Patel et al. 2004). The extraction method and clean-up are explained in Figure 1. In the recovery study, after the addition of TPP, ethyl acetate was added to the fortified samples to a final volume of 1 mL and transferred to a 2.00-mL vial compatible with the autosampler. The other samples were separated into two groups: the first group, termed ‘blank extract’, was constituted by nine blank samples

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Figure 1. Extraction and clean-up method. Table 1. Calibration curve samples preparation scheme.

Sample number

Sample name

Sample concentration (mg kg 1)

Blank extract volume (mL)

Fortified extract volume (mL)

Final volume of extract (mL)

1 to 4 5 to 8 9 to 12 13 to 16 17 to 20 21 to 24 25 to 28 29 to 32

PC_00 PC_01 PC_02 PC_03 PC_04 PC_05 PC_06 PC_07

0.0000 0.0050 0.0100 0.0200 0.0300 0.0500 0.0800 0.1000

400 380 360 320 280 200 80 0

0 20 40 80 120 200 320 400

400 400 400 400 400 400 400 400

Table 2. Recovery study samples preparation.

Sample ID

Sample label

Level

Concentration for recovery level (mg kg 1)

Volume of standard mix added (mL)

1 to 6 to 12 to 18 to 24 to 30

Rec_01 Rec_02 Rec_03 Rec_04 Rec_05

1 2 3 4 5

0.01 0.02 0.03 0.05 0.08

6.25 12.50 18.75 31.25 50.00

7 13 19 25

whose volume was made up to 1 mL with the same solvent; the second group consisted of five blank samples that after fortification by addition of 62.5 mL of pesticide standard mixture (4 ng mL 1) had the volume made up to 1 mL, affording a final concentration of 0.1 mg kg 1. This batch was labeled â&#x20AC;&#x2DC;fortified samplesâ&#x20AC;&#x2122;. Both groups of samples were used to prepare the calibration curve, as shown in Table 1. The recovery study samples were fortified as explained in Table 2.

Selectivity and specificity The solution was injected containing a mixture of all the analytes investigated in the method in five different samples and the correct identification was assessed for all the analytes at 0.05 mg kg 1. The method specificity was studied for all analytes from results of injection of 20 extracted blank samples. The results indicated the method capacity in identifying unequivocally all the analytes studied.

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Linearity of instrumental response Linearity is an important criterion in the selection of the most adequate regression model. Linear regression can only be used for calibration curves which have a linear form. All the validated analytes had determination coefficients (R2) greater than 0.99, indicating high correlation and linearity of the data over all the concentration range studied. The model of linear regression used for all the analytes was the weighted least squares (WLS) method (Miller and Miller 2002). The weight (wi) criteria applied in linear regression of all the instrumental results obtained was the inverse of the squared variance at each concentration level (wi ¼ 1 (S2) 1). This approach permitted more realistic results and with smaller distortion effects (Miller and Miller 2002). The use of seven calibration levels in this study enabled successful validation for a large number of analytes. This linear regression model associated with the design of the validation process, which was performed in four days by two analysts, allowed the evaluation of the method’s robustness in a condition of great variability of the process.

areas at the smaller concentration level in which the relative standard deviation of the results is inferior than or equal to 20% and recovery between 70% and 120%. LDðmg kg 1 Þ ¼ 3 ð½RSDðLCLÞ=100 Concentration ðLCLÞ

ð1Þ

It was carried out a study that proved to be possible the detection of analyte response at the calculated LOD. However, the signal to noise ratio presented was inferior to six for most of the analytes. The limit of quantification (LOQ) was calculated multiplying the value of the limit of detection by 3.33 as presented in the equation 2. The value corresponds to 10 times the standard deviation of instrumental response at the LCL, multiplied by the LCL concentration. LOQðmg kg 1 Þ ¼ 10 ð½RSDðLCLÞ=100 Concentration ðLCLÞ ¼ 3:33 LD ð2Þ

The sample preparation procedure for the validation process was repeated on 4 days and used materials and laboratory equipment in random conditions. The procedure was performed by one analyst on the 1st and 3rd days and by another analyst on the 2nd and 4th days. A great variability in the conditions was applied to measure the experimental results to evaluate the behaviour of the method at the real laboratory conditions.

The LOQ, which is the smallest amount of analyte that can be quantified with an acceptable level of precision and accuracy, can be found in some national and international documents as the highest accepted LOQ for the methodologies of quantification of these residues. However, this value should be inferior or equal to the maximum residue limit (MRL). When the LOQs determined were below the LCL, the former were taken as the respective LCL. For the analytes whose calculated LOQ was superior than LCL, concentrations immediately superior to the former LCL were adopted if RSD was inferior or equal to 20% and recovery between 70% and 120%. The results are presented in Table 3 (SANCO 2009). In Brazil, the normative instruction IN 21 (BRAZIL 2009) concerns the fixation of reference limits for registered pesticides monitoring in several cultures, including papaya, used in this work as representative matrix for the group of matrices with high water content. The MRL values for the validated analytes that are regulated in this normative are presented in Table 3, in which the unregistered analytes for this culture and the analytes whose use is prohibited in the Brazilian agriculture are also shown. All the analytes were validated with the aim to cover the same scope investigated in this type of matrices by surveillance agencies from countries which import Brazilian products.

Limits of detection and quantification

Calibration curve

The limit of detection (LOD) was calculated from the standard deviation of peak areas at the lower control limit (LCL), as presented in equation (1). RSD (LCL) values represent relative standard deviation of peak

The calibration curve for each analyte was constructed with samples of the five smallest concentration levels that presented satisfactory results for, at least, three of the four samples injected at each concentration level.

Homoscedasticity The homoscedasticity of calibration curve for each analyte was assessed using Bartley test. This test compares the critical F value (95% level of confidence) with the calculated F 0 value (quotient between maximum and minimum variance of residues). The fit for all analytes was assessed using the WLS method because all of them presented heteroscedasticity of instrumental data in one or more days of validation. The WLS method presented a more adequate fit correcting distortions caused by dispersed and lowaccuracy points (Miller and Miller 2002).

Robustness

142/158/200 264/306/335 181/183/219 124/152/304 181/183/219 181/183/219 181/277/292 123/286/290 212/285 260/277 258/314 318/333 269/297/323 246/274 255/283/285 125/145/147 123/164/219 243/261/317/345 128/173/191/259 183/185/341 159/227/356 141/181/197 221/232/373 140/142/342

Etoprophos Trifluralin HCH Alfa Diazinon HCH Beta Lindane Etrinphos Chlorpyrifos methyl Vinclozolin Fenitrotion Chlorpyrifos ethyl Pirimiphos ethyl Chlorfenvinphos Fentoate Procimidon Methidathion Flutriafol Endrin Propiconazole Bromopropylate Tetradifone Lambda cyhalotrin Pyrazophos Boscalid

158 306 181 152 181 181 277 286 212 277 314 318 323 274 283 145 123 243 173 183 159 181 221 140

13.14 13.65 14.54 15.55 15.92 16.56 16.92 18.27 18.44 19.6 19.99 20.77 21.77 22.09 22.15 22.75 23.24 24.62 26.39 26.61 28.7 29.94 30.66 31.02 34.82

Response time expected (min) 20 10 15 20 15 15 20 10 30 20 20 30 35 100 10 20 20 20 20 100 20 20 20 20

Dwell time (ms) 0.12 0.09 0.10 0.12 0.10 0.10 0.12 0.11 0.09 0.12 0.12 0.10 0.15 0.24 0.09 0.12 0.12 0.12 0.12 0.24 0.12 0.12 0.12 0.12

Scan time (s) a a b a b b b a b b a b b a a a 0.5 b a a 0.05 a a a

MRL (mg kg 1) 101.8 91.5 104.6 101.0 101.1 102.6 98.7 100.3 100.0 99.1 100.6 94.1 97.4 100.5 100.9 101.3 100.5 94.9 97.4 100.8 100.9 101.3 97.1 99.7

Recovery average (%) 3.4 9.0 13.6 9.8 5.3 2.6 8.5 9.8 3.5 4.8 11.2 8.9 7.4 5.0 2.8 1.5 2.8 11.9 6.8 2.1 8.1 4.1 4.1 2.3

RSD average (%)

0.0030 0.0013 0.0015 0.0034 0.0034 0.0024 0.0041 0.0028 0.0019 0.0038 0.0015 0.0029 0.0028 0.0028 0.0039 0.0047 0.0043 0.0045 0.0044 0.0017 0.0065 0.0041 0.0057 0.0046

LOD (mg kg 1)

0.0200 0.0044 0.0200 0.0200 0.0100 0.0081 0.0100 0.0094 0.0065 0.0100 0.0200 0.0097 0.0092 0.0094 0.0100 0.0157 0.0100 0.0200 0.0200 0.0056 0.0200 0.0100 0.0100 0.0100

LOQ (mg kg 1)

Note: MRL, minimum residue limit; RSD, relative standard deviation; LOD, limit of detection; LOQ, limit of quantification; a, Analytes not allowed for these cultures; b, Products whose use is prohibited in the Brazilian agriculture.

Fragments (m/z)

Analyte

Quantification (m/z)

Table 3. Parameters evaluated to define the best performance of the proposed method.

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Table 4. Estimated uncertainty of all the analytes.

Compound Boscalid Bromopropylate Chlorfenvinphos Chlorpyrifos ethyl Chlorpyrifos methyl Diazinon Endrin Etoprophos Etrinphos Fenitrotion Fentoate Flutriafol HCH Alfa HCH Beta Lambda cyhalotrin Lindane Methidathion Pyrazophos Pirimiphos ethyl Procimidon Propiconazole Tetradifone Trifluralin Vinclozolin

1st level (mg kg 1)

U (mg kg 1)

2nd level (mg kg 1)

U (mg kg 1)

3rd level (mg kg 1)

U (mg kg 1)

4th level (mg kg 1)

U (mg kg 1)

0.010 0.010 0.010 0.010 0.010 0.010 0.020 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.020 0.010 0.010 0.010 0.020 0.020 0.010 0.010

0.003 0.003 0.002 0.009* 0.017 0.039* 0.002 0.011* 0.004 0.002 0.003 0.003 0.022* 0.003 0.004 0.002 0.002 0.004 0.003 0.003 0.013 0.002 0.002 0.005

0.020 0.020 0.020 0.020 0.020 0.020 0.030 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.030 0.020 0.020 0.020 0.030 0.030 0.020 0.020

0.003 0.003 0.002 0.001 0.006 0.002 0.007 0.002 0.002 0.002 0.006 0.003 0.001 0.002 0.004 0.002 0.002 0.004 0.004 0.002 0.004 0.003 0.003 0.002

0.030 0.030 0.030 0.030 0.030 0.030 0.050 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.050 0.030 0.030 0.030 0.050 0.050 0.030 0.030

0.003 0.003 0.002 0.001 0.002 0.001 0.002 0.001 0.003 0.002 0.002 0.002 0.001 0.002 0.003 0.002 0.002 0.004 0.003 0.002 0.006 0.007 0.004 0.002

0.050 0.050 0.050 0.050 0.050 0.050 0.080 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.080 0.050 0.050 0.050 0.080 0.080 0.080 0.050

0.003 0.003 0.013 0.003 0.003 0.005 0.002 0.002 0.003 0.004 0.003 0.004 0.002 0.005 0.013 0.003 0.002 0.011 0.012 0.002 0.004 0.008 0.008 0.003

Note: *High dispersion of bias between the validation days.

The samples are considered valid or satisfactory if the signal-to-noise ratio presented by integration is superior to six and the ion relative intensities present results compatible with that obtained in ion ratio study and in agreement with the requirements of SANCO/10684/ 2009 document. The regression parameters and their relative uncertainties were calculated by using the WLS method.

Recovery and RSD The recovery and RSD studies were conducted with fortified samples at four concentration levels among the five concentration levels injected. Samples were selected from the four lowest concentration levels that presented satisfactory results for at least five of the six samples injected at each concentration level.

Estimation of measurement uncertainty The procedure adopted was derived from the reconciliation model that is a simpler and more realistic approach for estimating the measurement uncertainty (Ellison and Barwick 1998; ABNT 2003; EA 2003; Eurachem/CITAC 2000; IPAC 2005; Bittencourt 2007; INMETRO 2010b).The first step was to identify the significant components and to estimate how it influences the final result. The contributions for the

uncertainty were estimated in relation to the instruments used to measure weight and volume, the calibration curves and the precision and accuracy of the method in intermediate precision conditions.

Results and discussion All the mentioned aspects and performance parameters were evaluated and the results are presented in Tables 3 and 4. The results indicated recoveries in the range 91% to 105% and RSDs less than 20% for all the validated analytes. These values are in agreement with the requirements recommended in the EU document SANCO/10684/2009 that establishes values of medium recovery between 70% and 120% and stipulates that values of average RSD should be smaller or equal to 20%, as acceptable quality criteria for pesticide residue analysis. The results obtained under conditions of intermediate precision demonstrated the viability of the method validated to be used in routine analysis and high robustness of the method for all validated analytes. The results did not indicate the presence of any interference sufficient to indicate a false-positive result as well the absence of a significant increase in baseline due to matrix influences (INMETRO 2010a). In Figure 2, the chromatographic profile obtained for the pesticides trifluralin and pirimiphos-ethyl in samples of two different concentrations is illustrated.

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Figure 2. Chromatographic profiles of trifluralin (a) and pirimiphos-ethyl (b) for fortified concentrations 0.01 and 0.10 mg kg 1.

All the results from the studies for definition of the instrumental parameters and concerning the method performance are shown in the Tables 3 and 4. The estimated uncertainty associated with each concentration level used in the recovery study is shown in Table 4. All the instruments of weight and volume measurement used in validation were calibrated and verified periodically. Even so, their contribution to the uncertainty proved to have little significance in relation to the final result. In carrying out these procedures, the equipment and materials were used randomly, enabling to observe a large variability in the obtained results. This made possible the evaluation of their influence on precision, accuracy and uncertainty estimated from the regression coefficients and their correlation. This procedure was established so that the contributions for estimating uncertainty are not underestimated. Conclusions The validation procedure was efficient because it allowed evaluation of the performance and to estimate the uncertainty for the method under intermediate precision conditions in accordance with national and international recommendations and normatives. The results were satisfactory, indicating the adequacy of the methodology validated with the materials and equipment available in the laboratory. Propiconazole gave two different chromatographic signals because of optical isomers that could not be

integrated together. In this case, each signal was integrated in a different way and the instrumental responses of the two signals were added together for each sample. The signals of -HCH and lindane ( -HCH) had retention times that were very close, which made the simultaneous analysis of these analytes difficult. The incorporation of new analytes and matrices will be carried out to extend the scope for this method with the aim to cover a wide range of analytes and matrices.

References [ABNT] ABNT, INMETRO, ISO, GUM. 2003. Guide to the expression of uncertainty in measurement. 2a ed. Brazilian Association of Technical Standards. [ABNT] ABNT, NBR, ISO/IEC 17025. 2005. General requirements for the competence of calibration and testing laboratories. Brazilian Association of Technical Standards. Bitencourt R. 2007. Uncertainty in chemical analysis: alternative approaches. Portugal: National Institute of Biological Resources. Pesticide Residue Laboratory/ Directorate General for Crop Protection [LRP/DGPC]. [BRAZIL] Ministry of Agriculture, Livestock and Supply. 2009. National Plan of residues and contaminants in vegetal products. Normative instruction no. 21. [EA] European Co-operation for Accreditation. 2003. EA 4/16 guidelines on the expression of uncertainty in quantitative testing. EA Laboratory Committee.

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Ellison SLR, Barwick VJ. 1998. Estimating measurement uncertainty: reconciliation using a cause and effect approach. Accred. Qual. Assur. 3:101–105. Eurachem/CITAC Guide. 2000. Quantifying uncertainty in analytical measurement. 2nd ed. [INMETRO] National Institute of Metrology, Standardization and Industrial Quality. 2010a. Guidelines on validation of analytical methods. DOQCGCRE-008. [INMETRO] National Institute of Metrology, Standardization and Industrial Quality. 2010b. Expression of uncertainty in measurement. Brazilian version of the document EA-4/02 Expression of Uncertainty of Measurement in Calibration. NITDICLA-021. [IPAC] Portuguese Institute for Accreditation. 2005. Guidelines for the accreditation of chemicals laboratories. Normative OGC002. [IPAC] Portuguese Institute for Accreditation. 2007. Guide to uncertainty measurement in chemical testing. Normative OGC007. Miller JN, Miller JC. 2002. Statistics for analytical chemistry. 4th ed. New York: Prentice Hall. Pang GF, Fan CL, Liu YM, Cao YZ, Zhang JJ, Fu BL, Li XM, Li ZY, Wu YP. 2006. Multi-residue method for

determination of 450 pesticide residues in honey, fruit juice and wine by double-cartridge solid-phase extraction/gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry. Food Addit Contam. 23(8):777–810. Patel K, Fussell RJ, Macarthur R, Goodall DM, Keely BJ. 2004. Method validation of resistive heating-gas chromatography with flame photometric detection for the rapid screening of organophosphorus pesticides in fruit and vegetables. J Chromatogr A. 1046(1):225–234. [SANCO] European Commission. 2009. Method validation and quality control procedures for pesticide residues analysis in food and feed. Document No. SANCO/ 10684/2009. Thompson MSLR, Ellison A, Fajgelj P, Willetts RW. 2000. Harmonised guidelines for the use of recovery information in analytical measurement. IUPAC/ISO/AOAC International/Eurachem. [WHO] World Health Organisation. 2003. Guidelines on good laboratory practice in residue analysis CAC/GL 40-1993 (rev. 1). Rome (Italy): FAO/WHO. [WHO] World Health Organisation. 2005. Guidelines on the use of mass spectrometry (MS) for identification, confirmation and quantitative determination of residues CAC/GL 56-2005. Rome (Italy): FAO/WHO.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 665–678

A multi-residue method for the determination of 90 pesticides in matrices with a high water content by LC–MS/MS without clean-up Fernando Diniz Madureira*, Fabiano Aure´lio da Silva Oliveira, Wesley Robert de Souza, Ana Paula Pontelo, Mauro Lu´cio Gonc¸alves de Oliveira and Gilsara Silva Ministry of Agriculture, Livestock and Food Supply of Brazil, Laborato´rio Nacional Agropecua´rio (LANAGRO/MG), Pesticide Laboratory, Av. Roˆmulo Joviano, s/n Pedro Leopoldo, MG 33600-000, Brazil (Received 26 November 2010; final version received 31 August 2011) A method using QuEChERS extraction and LC–MS/MS in electrospray positive ionisation mode was developed and validated for the analysis of 90 pesticides in a high water content matrix (tomato) in a single chromatographic run. To assess the intra-laboratory reproducibility of the method, validation was conducted on four different days by two different analysts. The validation data was treated using a spreadsheet developed in-house, which sets the most appropriate model for linear fit by determining whether the residuals of the calibration curves are homocedastic or heterocedastic. A statistical test for the significance of regression was also carried out. Calibration was always matrix-matched and the curves were obtained over the range 0.0075–0.10 or 0.020–0.125 mg kg 1. Identification of analytes was based on retention times and MRM ratios. Recoveries were assessed at four different levels for each analyte and were between 73 and 106%, with relative standard deviations under reproducibility conditions of 520%. The measurement uncertainties of the method for each pesticide analysed were below 50%. Previous validation of the same method, applied to papaya samples and satisfactory results obtained in various proficiency tests with different high water content matrices, demonstrated the applicability of the method to these classes of commodities, without clean-up. The validated method will be applied routinely in the pesticide residues monitoring programme that constitutes the National Residue and Contaminant Control Plan of Brazil. Keywords: vegetables; fruit; pesticide residues; LC/MS

Introduction The use of pesticides in modern agriculture is necessary to guarantee the production of food in suitable quantities, as they are an effective tool in reducing losses in crop production due to attacks by plant pests (Bhanti et al. 2007). Although pesticides play an important role in increasing food production, the risks they pose to humans and the environment should be considered. Pesticides are linked to various chronic health problems, such as cancer, neurologic diseases and adverse reproductive effects (Quackenbush et al. 2006). Exposure to pesticides, both in the working environment and their dietary intake, is a problem of increasing concern with respect to human health. This has lead to modifications in crop-protection strategies and to a great emphasis on food quality and safety (Hu´skova´ et al. 2008). Brazil, as a major food producer and with a tropical climate, which makes agriculture production more susceptible to pests, is ranked as one of the largest consumers of pesticides (Chrisman et al. 2009).

Brazil is ranked third in the export of agricultural goods, while products of vegetal origin represent more than 70% of the total (Brasil/Mapa 2009). Brazilian agribusiness also recognises the importance of food quality and safety, as it can be seen by the investment in research and measures to prevent and combat plant pests. Monitoring programmes are important to guarantee consumer’s safety and facilitate international trade (Lee et al. 2008). To produce high quality foodstuffs, the federal Government of Brazil has increased the monitoring of products via the National Residue and Contaminant Control Plan (NRCCP). Over the last decade, the expansion of the analytical capacity of Brazilian laboratories has resulted in a substantial increase in the number and nature of tested samples, as well as in the chemicals being analysed (Mauricio et al. 2009). In 2009, with the publication of normative instruction No. 21 (Brasil 2009), the residue and contaminant control programme for vegetable products was expanded, which widened the scope of analytical methods of the official

*Corresponding author. Email: fernando.madureira@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.623837 http://www.tandfonline.com

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laboratories. This increased analytical burden will be a constant challenge for laboratories to ensure the efficient evaluation of quality in agricultural products. The official laboratories involved in monitoring programs require analytical methods that are costeffective, provide reliable results and are able to monitor as many pesticides simultaneously as possible. In the past, most pesticides were analysed by GC with various specific detectors (Venkateswarlu et al. 2007). With the development of new pesticides that are semipolar and less volatile, LC has become the technique of choice in pesticide residue analysis. The introduction of tandem mass spectrometry (MS/MS) with atmospheric pressure ionisation revolutionised the analysis of residues by combining all the advantages of LC with the high sensitivity and selectivity inherent in MS/MS. Other advantages of LC–MS/MS include simpler clean-up steps, good ability in discriminating analytes and matrix signals, and the possibility of multi-residue analysis with a great number of analytes (Hiemstra et al. 2007). Quick, easy, cheap, effective, rugged and safe (QuEChERS) extraction is the most common method of pesticide residue extraction, due to its dynamicity and simple steps, which demand a low volume of solvent compared to other methods (Anastassiades et al. 2003; Prestes et al. 2009). Furthermore, the significant reduction in time needed for processing samples via QuEChERS make it possible to take advantage of the high sample throughput offered by ultra performance liquid chromatography (UPLC) and fast gas chromatography (Cajka et al. 2008; RomeroGonza´lez et al. 2008). QuEChERS was originally developed as a field of application for residue analysis of pesticides. In recent years, however, the method has also been applied, with satisfactory results, to the analysis of other contaminants and residues, such as mycotoxins, veterinary drugs, steroids and plant toxins, in different matrices, such as milk, animal tissues, beverages, honey, egg and animal feed. (Mol et al. 2008; Stubbings et al. 2009; Klinsunthorn et al. 2011; Tamura et al. 2011). In the present study, we have developed and validated a method for analysis of 90 pesticide residues in watery vegetal samples with the aim of complying with NRCCP of Brazil. Tomato was selected to represent a high water content matrix and as a product that is widely cultured all over the country (production was 3.9 million tonnes in 2008) (Faostat 2008). The pesticides included in this study were those laid down in NRCCP and comprised some active substances whose use is prohibited in Brazilian agriculture and others which are not allowed in tomato cultivation. The application of QuEChERS in the multi-residue analysis of pesticides in samples with high water content using LC–MS/MS has been described previously (Kme´llar et al. 2008; Romero-Gonza´lez

et al. 2008). Although the present study deals with the determination of pesticide residues, it also demonstrates a novel and rugged statistical approach to the validation procedure. It presents an analytical method that is suitable for checking MRL compliance of plant produce classified as being in the high water content group according to the Codex classification with no need for a clean-up step.

Materials and methods Chemicals, reagents and materials Methanol (HPLC grade) was obtained from Mallinckrodt (Phillipsburg, PA, USA) and Merck. Acetic acid PA, ammonium acetate PA and anhydrous sodium acetate PA were acquired from Vetec (Rio de Janeiro, Brazil). Ultrapure water, for preparation of the aqueous mobile phase, was obtained via a Direct 3Q UV purification system (Millipore, Molsheim, France). Acetonitrile, pesticide grade, was purchased from Tedia (Fairfield, OH, USA). Pesticide reference standards were acquired from Sigma-Aldrich/Fluka/ Riedel-de-Hae¨n (Seelze, Germany). Tomato samples used as blank matrix were organically produced and determined to be free of pesticide residues.

Preparation of standard solutions The stock solutions were prepared by dissolving the reference standards in methanol to obtain a concentration of 1000 ng ml 1. Only carbendazim stock solution was prepared at a concentration of 100 ng ml 1. A mixed working standard solution was prepared by diluting the stock solutions to obtain a solution with a concentration of 2 ng ml 1 for all analytes in acetonitrile. All standard solutions were stored in a freezer at a temperature of 20 C and left to achieve room temperature prior to use.

Sample preparation Blank samples were comminuted using a Foss homogenizer (model 2096; Ho¨gana¨s, Sweden). The comminuted samples were transferred to containers and stored in a freezer at 20 C. The extraction procedure was based in the original QuEChERS method (Anastassiades et al. 2003) with some adaptations. Briefly, 10.0 0.1 g of the comminuted sample was shaken vigorously for 1 min with 10 ml of acetonitrile containing 1.0% acetic acid in a 50-ml polypropylene conical centrifuge tube. Then, 4.0 g of anhydrous magnesium sulfate and 1.0 g of anhydrous sodium acetate were added to the tube which was vortexed immediately for 1 min. After centrifugation at 4000 rpm for 9 min, the supernatant was transferred to another centrifuge tube containing 1.5 g of

Food Additives and Contaminants anhydrous magnesium sulfate. The tube was vortexed for 30 s and then centrifuged again at 4000 rpm for 9 min. An aliquot of the extract was finally transferred to a vial for injection into the LC–MS/MS system.

Recovery study For determination of the recovery, 10 g of the homogenized blank samples were spiked with adequate aliquots of the mixed standard solution to a final concentration of 0.01, 0.05, 0.075, 0.10 and 0.125 mg kg 1. After spiking, the tubes containing the samples were shaken and sonicated for 5 min to guarantee homogeneous distribution of pesticides through the material. They were then extracted according to the procedure described above. The spike procedure was repeated 6-fold for each concentration level.

Matrix-matched calibration All the calibration solutions were matrix-matched. Aliquots of 1 ml of tomato blank extract were evaporated near to dryness using a centrifugal evaporator. Appropriate volumes of the working standard solution containing all the analytes were added to the evaporated extracts and the volume was completed to 1 ml with acetonitrile. The calibration concentrations were 7.5, 10, 20, 50, 75, 100 and 125 ng ml 1, which corresponds, respectively, to 0.0075, 0.01, 0.02, 0.05, 0.075, 0.10 and 0.125 mg kg 1 (expressed as mg of the analyte per kg of sample). The calibration solutions were also prepared 6-fold for each concentration level.

Instrumentation An Agilent 1100 HPLC system (Agilent Technologies, Waldbronn, Germany) equipped with a quaternary pump, degasser, autosampler and column heater was used for LC analyses. The columns were C18 with different characteristics and from distinct manufacturers: SymmetryÕ (150 2.1 mm I.D., 3.5 mm particle size) from Waters; LunaÕ (150 3.0 mm I.D., 3 mm particle size) from Phenomenex; Zorbax Eclipse XDBÕ (150 4.6 mm, 5 mm particle size) from Agilent. The flow-rate varied from 0.2 to 1.0 ml min 1 according to the diameter of the column used. Various elution gradients were tested, using 10 mmol l 1 aqueous ammonium acetate (mobile phase A) and unmodified methanol or acetonitrile (mobile phase B). Mass spectrometric analyses were performed on a 4000 QTrap MS/MS system (MDS Sciex/Applied Biosystems, Ontario, Canada). The mass spectrometer was operated using the electrospray source in positive mode. The analyte-dependent parameters were optimised by direct infusion of very dilute solutions of each

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compound to determine the declustering potential (DP) and collision energy (CE). Table 1 shows the quantification and identification ions for all the studied compounds, as well as their retention time. Source parameters, such as ion spray voltage, temperature and pressure of nebuliser gas and auxiliary gas were optimised by flow injection analysis for all the flowrates used during method optimisation. Data were acquired using Analyst software, version 1.4.2.

Method validation The method was developed to analyse 90 pesticides in a single chromatographic run. Validation involved repetition of all the steps (extraction, preparation of calibration curve and recovery study) on four different days by two different analysts over 4 weeks, to assess intra-laboratory repeatability and reproducibility of the method. Quantification was based on peak areas. Validation was performed with six replicates for each level of the calibration curve, as well as for each spiked level on each day. During data treatment, of the seven calibration levels, the five lowest levels affording acceptable response, i.e. signal-to-noise ratio of at least 3, were selected to build the calibration curve for each analyte. After removal of outlier values using Grubbs’ test in each level, a F-Snedecor test was applied to check the homocedasticity of variance of residuals across the range selected (Meyer et al. 2000). The model for the regression curve depended on the result of the homocedasticity test; for homocedastic data, a linear fit using ordinary least-squares (OLS) method was applied and, when heterocedasticity was identified, the model adopted for regression was the weighted least-squares (WLS) model. For the WLS method, the analytical response was weighted by the inverse of the variance (wi ¼ 1/s2) (Souza et al. 2005). Since the linear curve was assessed using the most appropriate model, a t-test was performed to evaluate the adequacy of the determination coefficient to the proposed model. An analysis of variance of residuals of the calibration curve was also run to evaluate the significance of regression. The significance level for all tests was 95%. The test for significance of regression allowed us to accept linear regressions with determination coefficients 50.9, which is, generally, the only parameter evaluated for the adequacy of linear fit in studies involving validation of methods for pesticide analysis (Pizzutti et al. 2007; Kme´llar et al. 2009). The procedure of testing the significance of regression is particularly important for compounds that exhibit a variable response, what might lead to a determination coefficient unequivocally considered inadequate. All data treatments were performed using an Excel spreadsheet developed in-house for validation purposes.

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Table 1. Pesticides analysed by LC–MS/MS (ESI positive mode) and their respective monoisotopic masses, precursor ions, MRM transitions, MS parameters, ion ratio data and average retention times.

Pesticide 3-Hydroxycarbofuran Acetamiprid Aldicarb Aldicarb sulfone Azinphos ethyl Azinphos methyl Azoxystrobin BF 500-3 Bifentrine Bioallethrin Boscalid Carbendazin Carbofuran Carbophenothion Carbosulfan Chlorpyrifos Clorfenvinphos Cyazofamid Cyproconazole Cyprodinil Deltamethrin Diazinon Dichlofluanid Difenoconazole Dimethoate Ethion Ethoprophos Ethoxysulfuron Ethyl parathion (parathion) Etrimfos Fenamidone Fenamiphos Fenamiphos sulfone Fenamiphos sulfoxide Fenarimol Fenhexamid Fenvalerate Fipronil Fluazifop p-butyl Flutriafol Foramsulfuron Furathiocarb Hexaconazole Hexythiazox Imazalil Imidacloprid Indoxacarb Iprodione Iprovalicarb Isoproturon Kresoxim-methyl Linuron Malathion Metalaxyl Methidathion Methomyl Metsulfuron methyl Monocrotophos Myclobutanil Omethoate Oxamyl Penconazol Pendimethalin Permethrin, cis and I Phenthoate

Monoisotopic mass

Precursor ion

237 222 190 222 345 317 403 357 422 302 342 191 221 342 380 349 358 324 291 225 503 304 332 405 229 384 242 398 291 292 311 303 335 319 330 301 419 436 383 301 452 382 313 352 296 255 527 329 320 206 313 248 330 279 302 162 381 223 288 213 219 283 281 390 320

[M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ [M þ H]þ [M þ NH4]þ [M þ H]þ

Quantification Confirmatory Ion transition CE transition CE DP ratio 238 ! 220 223 ! 126 208 ! 116 223 ! 148 346 ! 160 318 ! 132 404 ! 372 358 ! 164 440 ! 181 303 ! 151 343 ! 307 192 ! 160 222 ! 165 343 ! 157 381 ! 160 350 ! 198 359 ! 155 325 ! 108 292 ! 125 226 ! 93 523 ! 281 305 ! 169 333 ! 123 406 ! 251 230 ! 199 385 ! 199 243 ! 173 399 ! 261 292 ! 236 293 ! 125 312 ! 236 304 ! 217 336 ! 266 320 ! 233 331 ! 268 302 ! 97 437 ! 393 437 ! 368 384 ! 282 302 ! 70 453 ! 182 383 ! 195 314 ! 70 353 ! 228 297 ! 159 256 ! 209 528 ! 249 330 ! 245 321 ! 119 207 ! 72 314 ! 267 249 ! 160 331 ! 127 280 ! 192 303 ! 145 163 ! 88 382 ! 167 224 ! 193 289 ! 70 214 ! 183 237 ! 72 284 ! 70 282 ! 212 408 ! 183 321 ! 247

16 30 11 15 23 23 18 20 20 14 29 27 19 18 29 30 19 20 53 50 22 32 18 39 15 15 21 25 22 24 20 32 29 32 34 33 15 25 29 33 32 31 47 23 34 28 24 22 14 34 12 27 19 26 13 16 24 13 49 17 27 37 17 29 19

238 ! 181 223 ! 56 208 ! 89 223 ! 166 346 ! 132 318 ! 160 404 ! 344 358 ! 132 440 ! 166 303 ! 123 343 ! 140 192 ! 132 222 ! 123 343 ! 199 381 ! 118 350 ! 115 359 ! 127 325 ! 261 292 ! 70 226 ! 108 523 ! 181 305 ! 153 333 ! 224 406 ! 188 230 ! 171 385 ! 171 243 ! 131 399 ! 218 292 ! 264 293 ! 265 312 ! 92 304 ! 234 336 ! 308 320 ! 171 331 ! 139 302 ! 55 437 ! 167 437 ! 290 384 ! 328 302 ! 123 453 ! 272 383 ! 252 314 ! 159 353 ! 168 297 ! 201 256 ! 175 528 ! 203 330 ! 288 321 ! 203 207 ! 165 314 ! 238 249 ! 182 331 ! 285 280 ! 220 303 ! 85 163 ! 106 382 ! 199 224 ! 127 289 ! 125 214 ! 155 237 ! 90 284 ! 159 282 ! 194 408 ! 355 321 ! 163

Dwell time (s)

Average retention time (min)

10 59 0.670 0.008 2.66 31 58 0.108 0.008 2.72 23 28 0.875 0.008 4.20 15 60 0.599 0.008 1.71 13 36 0.843 0.008 11.45 14 41 0.178 0.008 8.89 40 50 0.157 0.008 9.15 41 58 0.688 0.008 14.00 58 42 0.332 0.008 22.70 24 59 0.811 0.008 17.02 30 73 0.253 0.008 9.90 43 81 0.189 0.008 3.81 29 58 0.762 0.008 5.43 14 42 0.193 0.008 18.39 21 59 0.984 0.008 21.61 33 58 0.343 0.008 17.78 27 62 0.392 0.008 13.40 and 15 52 0.172 0.008 11.81 57 60 0.422 0.008 10.52 and 37 87 0.601 0.008 13.34 52 60 0.110 0.008 19.52 29 102 0.235 0.008 13.44 38 61 0.485 0.008 11.12 67 71 0.141 0.008 13.67 and 22 47 0.292 0.008 2.97 24 53 0.883 0.008 17.22 30 83 0.415 0.008 11.57 36 110 0.286 0.008 4.53 15 62 0.392 0.008 12.68 36 90 0.931 0.008 13.29 39 105 0.256 0.008 9.52 23 48 0.867 0.008 12.31 21 75 0.862 0.008 5.73 35 35 0.904 0.008 5.35 52 82 0.444 0.008 11.40 62 81 0.950 0.008 11.13 27 44 0.225 0.008 19.41 39 85 0.260 0.008 12.18 26 70 0.553 0.008 16.15 62 50 0.488 0.008 7.33 20 67 0.228 0.008 2.67 19 57 0.861 0.008 16.37 23 60 0.261 0.008 13.80 36 55 0.692 0.008 17.61 28 56 0.567 0.008 13.27 28 57 0.987 0.008 2.29 55 70 0.438 0.008 14.86 19 65 0.179 0.008 12.16 33 41 0.656 0.008 11.13 21 56 0.409 0.008 7.79 14 43 0.217 0.008 12.76 24 61 0.498 0.008 9.49 12 60 0.748 0.008 10.36 21 44 0.736 0.008 7.69 31 31 0.408 0.008 8.46 16 34 0.606 0.008 1.92 32 53 0.134 0.008 1.93 23 46 0.617 0.008 1.92 49 58 0.468 0.008 10.62 23 48 0.408 0.008 1.57 12 30 0.427 0.008 1.67 39 50 0.661 0.008 12.89 27 36 0.157 0.008 17.97 13 36 0.275 0.008 21.06 and 17 56 0.305 0.008 12.65

14.04 11.18

14.74

22.07

(continued )

Food Additives and Contaminants

669

Table 1. Continued.

Pesticide Phorate Phosalone Phosmet Picolinafen Pirimicarb Pirimiphos-ethyl Prochloraz Profenofos Propiconazole Propoxur Pyraclostrobin Pyrazophos Pyridaben Pyridate Pyrimethanil Spiroxamine Tebuconazole Tebufenozide Thiabendazole Thiacloprid Thiamethoxam Triasulfuron Triazophos Trichlorfon Trifloxystrobin

Monoisotopic mass 260 367 317 376 238 333 375 372 341 209 387 373 364 378 199 297 307 352 201 252 291 401 313 256 408

Precursor ion [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ [M þ H]þ

Quantification Confirmatory Ion transition CE transition CE DP ratio 261 ! 75 368 ! 182 318 ! 133 377 ! 238 239 ! 72 334 ! 198 376 ! 266 373 ! 303 342 ! 159 210 ! 168 388 ! 194 374 ! 222 365 ! 309 379 ! 207 200 ! 107 298 ! 144 308 ! 70 353 ! 133 202 ! 175 253 ! 126 292 ! 211 402 ! 167 314 ! 162 257 ! 127 409 ! 186

The LOD was estimated from the calibration curves at the lowest calibration level that presented a response for all six replicates. The limit of detection was defined as LOD (ng ml 1) ¼3 RSD% concentration, where RSD is the relative standard deviation of the average response. For this approach to be valid, the blank must have presented no signal for the analyte (Pizzutti et al. 2009). The LOQ was defined as the lowest spiking level that met the performance criteria, i.e. mean recoveries between 70 and 120% with relative standard deviation determined under reproducibility conditions of 20% (European Commission 2009). Accuracy and precision were evaluated through recovery experiments. Intralaboratory reproducibility was assessed as the relative standard deviation of the average recoveries calculated from the results obtained from spiked samples carried out over four different days by two analysts and using six replicates at each fortification level. The reproducibility data was also used to estimate the measurement uncertainty for each analyte.

Results and discussion Definition of instrument parameters and chromatographic conditions The mass spectrometer parameters were optimised with the aim of (1) obtaining a protonated molecule or

18 20 52 41 39 33 17 27 43 21 18 29 38 30 35 30 52 13 39 29 19 25 26 25 25

261 ! 199 368 ! 111 318 ! 160 377 ! 256 239 ! 182 334 ! 182 376 ! 308 373 ! 345 342 ! 69 210 ! 111 388 ! 163 374 ! 194 365 ! 147 379 ! 351 200 ! 82 298 ! 100 308 ! 125 353 ! 297 202 ! 131 253 ! 186 292 ! 181 402 ! 141 314 ! 119 257 ! 221 409 ! 206

12 55 18 34 23 32 25 19 37 12 35 45 20 15 37 46 55 27 47 21 32 29 48 17 21

43 58 45 84 57 60 41 66 70 42 42 81 55 54 74 62 63 35 75 60 51 66 67 59 56

0.247 0.252 0.109 0.271 0.662 0.581 0.196 0.645 0.556 0.553 0.649 0.771 0.199 0.449 0.853 0.002 0.081 0.629 0.673 0.152 0.525 0.654 0.212 0.973 0.373

Dwell time (s)

Average retention time (min)

0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008

14.30 14.09 8.87 16.67 6.94 16.91 14.05 15.97 12.07 and 13.42 5.27 13.86 14.38 20.11 21.47 9.73 18.57 13.13 and 14.06 12.32 4.81 3.22 1.91 2.61 10.96 2.95 14.91

an ammonium adduct, in case the latter afforded a more sensitive response, and (2) selecting the product ions that were characteristic for the molecule analysed or presented a higher m/z ratio, to avoid the disruptive effects of the matrix (Kmella´r et al. 2008). Optimisation of the precursor and product ions was accomplished through direct infusion of solutions in methanol/water (1:1, v/v) at 0.5 mg ml 1. Some compounds required even more diluted solutions. DP and CE parameters were optimised for various product ions, and the most two intense ions that fulfilled the conditions outlined above were selected to build the acquisition method. The choice of quantification and confirmatory transitions was made after a chromatographic run, as some transitions that were more intense in the mass spectra did not give the peak with the largest area in the chromatograms. The quantification transition selected was always that which presented the highest response (the best signal-to-noise ratio) and no evidence of chromatographic interference. One approach to obtain optimum sensitivity in chromatography is by using columns with small particle sizes, which can increase column efficiency and give narrower peaks. This sensitivity is, however, limited by the sample volume that can be injected into the chromatographic system. A comparison of the three columns after several injections of 10 mg ml 1 standard solution in acetonitrile allowed an assessment of the column with the best performance, regarding

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resolution and peak shape. For the conditions tested, the Zorbax Eclipse XDB (4.6 150 mm, 5 mm) was the most suitable for multi-residue analysis when an injection volume of 10 ml was used. For the column selected, the flow-rate giving the best performance was at 1 ml min 1. Choosing the best chromatographic conditions demanded the use of different flow-rates and, thus, the source parameters had to be optimised previously for each flow-rate. For 1 ml min 1, the source parameters were: capillary voltage 5500 V, source temperature 550 C, curtain gas 30 psi, nebuliser gas (N2) 60 psi and auxiliary gas (N2) 60 psi. The ions were fragmented using N2 as collision gas at 5 psi. Various elution gradients were also tested, using methanol or acetonitrile as the organic mobile phase; the aqueous phase was always constituted by 10 mmol l 1 ammonium acetate. The gradient started with 50% of each mobile phase and was ramped to 5% methanol until 23 min. The conditions were maintained for the next 7 min. After returning to the initial conditions in 2 min, the column was re-equilibrated for 3 min, giving a total run time of 35 min. The gradient used is showed in Table 2. The total ion chromatogram obtained for all pesticides at 100 ng ml 1 in tomato blank extract is shown in Figure 1.

Comparison of MRM ratio Pesticides were identified according to their retention times, quantification and identification ions and ion ratio. The use of a mass spectrometer in multiple reaction monitoring (MRM) mode is a powerful approach for the identification of substances, due to its high specificity, as the chromatogram is generated by specific ions that fragment to produce other specific ions. The relative ion intensity or MRM ratio is an important criterion to distinguish between an analyte and other co-eluents or matrix interferences. The use of MRM ratio as a confirmation criterion is possible when the relative ion intensities in the sample correspond to the calculated intensity of the calibration standards or spiked samples, at comparable concentrations. The MRM ratio was calculated by dividing the peak areas of extracted ion chromatogram for the confirmatory transition by the peak areas of the extracted ion chromatogram for the quantification transition. The maximum permitted tolerances were calculated according to Document No. SANCO/ 10684/2009. For some analytes, it was not possible to determine the ion ratios at the lowest levels owing to the low intensity of the confirmatory transition signal, as was the case for deltamethrin, fenvalerate, fipronil and spiroxamine. For the latter compound, the second transition gave very low responses for all levels. The

Table 2. Gradient elution conditions for multiresidual pesticide analysis (Flow-rate 1 ml min 1).

Time (min) 0 5 9 15 23 30 32 35

% Aqueous ammonium acetate 10 mmol l 1

% Methanol

50 35 25 14 5 5 50 50

50 65 75 86 95 95 50 50

Figure 1. Total ion chromatogram obtained by LCâ&#x20AC;&#x201C;MS/MS (ESI positive mode) of a calibration solution at 125 ng ml 1 in blank tomato extract.

ion ratios are shown in Table 1, all of which meet the permitted tolerances.

Linearity, calibration curves, selectivity and LOD After integration of the chromatograms, the five lowest levels affording a single-to-noise ratio of at least 3 were selected for each analyte for evaluation of linearity. For 13 analytes giving a poor response at the two lowest levels studied (0.0075 and 0.01 mg kg 1), the range for calibration was constituted by levels of 0.02, 0.05, 0.075, 0.10 and 0.125 mg kg 1. For all other analytes, levels of 0.0075, 0.01, 0.02, 0.05 and 0.10 could be used to build the calibration curves. Analysis for homo/heterocedasticity showed that 92% of the data analysed were heterocedastic, so that all these curves were adjusted to a linear fit using the weighted least-squares (WLS) method. For the remaining curves, the ordinary least-squares (OLS) method was used. It was observed that some analytes presented data which varied between heterocedastic and homocedastic behaviour; thus, all routine data generated

Food Additives and Contaminants were analysed to find the most suitable way of estimating the unknown parameters in a linear regression model. Figure 2 shows calibration curves for 3hydroxycarbofuran and boscalide, which were fitted to a linear regression using the OLS and WLS methods, respectively. Note that, in the calibration curve for boscalide, there is a greater dispersion of the data plotted for the highest concentration levels, which makes WLS most suitable for heterocedastic data. Over the calibration ranges selected, all the calibration curves presented significant linearity according to the t-test on r2 and the test for significance of regression (analysis of variance of the residuals of the curve), despite some determination coefficients (r2) remained below 0.9. The determination coefficients were between 0.8285 and 0.9998. Some studies (KmellaÂ´r et al. 2008) do not accept r2 5 0.9, but, in this work, pesticides presenting calibration curves in this situation were not deleted from the list. Figure 3 depicts an individual MRM chromatogram for malathion and illustrates the method selectivity. For the first transition, the chromatogram obtained from the blank tomato extract shows the presence of four interfering peaks arising from matrix compounds, while no interfering peak is observed for the second transition. As can be seen in the chromatograms produced from the calibration standard solution at 7.5 ng ml 1 in the blank matrix extract and from the spiked sample at 0.01 mg kg 1, the peak for malathion is completely resolved from the matrix interfering peaks, demonstrating the selectivity of the method. Similar behaviour was achieved for all analytes, i.e. it was always possible to obtain peaks for the analytes that appeared in regions of the chromatograms not presenting matrix-interfering peaks. This result was achieved without the use of a clean-up step, which reduces the analytical procedure time and contributes to greater accuracy of the method by involving a fewer number of steps. The limits of detection (LOD) were determined between the range 1 and 20 ng ml 1. A total of 92% of the pesticides had a LOD 5 10 ng ml 1, which corresponds to 10 mg of analyte per kg of the sample. The higher LOD values are due to the low sensitivity of some analytes at the lowest levels. Since LODs were calculated from the RSD at the lowest levels of the calibration curves, the variability in responses occasioned by low sensitivity led to higher LODs for carbosulfan, dichlofluanid, iprodione, linuron, metsulfuron methyl, phorate and trichlorfon.

Method LOQ, precision and accuracy The method performance was evaluated according to the requirements presented in Document No. SANCO/ 10684/2009. Recoveries in the range 70â&#x20AC;&#x201C;120% with

671

Figure 2. Calibration curves for (a) 3-hydroxycarbofuran and (b) boscalide fitted by OLS and WLS methods, respectively.

RSD in reproducibility conditions 20% have to be met. Recoveries below 70% can be accepted if the RSD remains 20% and the basis for this is well established (European Commission 2009). Regarding the spiking levels that were prepared and analysed to assess the accuracy of the method, for the analytes whose calibration curves were set from 0.0075 to 0.10 mg kg 1, the recovery was determined using the spiking levels at 0.01, 0.05, 0.075 and 0.10 mg kg 1. For those compounds whose calibration curve was set from 0.02 to 0.125 mg kg 1, the recovery was determined at 0.05, 0.075, 0.10 and 0.125 mg kg 1. Table 3 shows recoveries and precision results for all 90 pesticides studied at four different levels according to the analytical range established for each analyte. Three pesticides did not fulfill at least one of the requirements: carbosulfan, dichlofluanid and iprodione. Carbosulfan did not comply with the criteria for all spiking levels studied, showing recoveries below 70% for two spiking levels and RSD 4 20 % for all levels. Dichlofluanid presented an RSD 4 20% for the three lowest levels, showing an acceptable performance only at 0.125 mg kg 1 and, thus, met the criteria at only one spike level. Iprodione showed recovery within the

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Figure 3. Extracted ion chromatograms for malathion obtained by LC–MS/MS (ESI positive ion mode): (a) 1st and (b) 2nd MRM transitions for blank tomato extracts; (c) 1st and (d) 2nd MRM transitions for standard solution at 7.5 ng ml 1 in blank tomato extract; (e) 1st and (f ) 2nd MRM transitions for tomato spiked at 0.01 mg kg 1.

acceptable range for all the spike levels, but it did not present a RSD 5 20% at 0.05 mg kg 1. All the other 87 pesticides showed satisfactory performance. About 80% of the pesticides had a method LOQ of 10 mg kg 1. Those who had LOQ greater than 10 mg kg 1 were the pesticides whose calibration range was initiated at 0.02 mg kg 1. The recovery and/or RSD criteria were not met by some analytes for all the spiking levels, such as carbosulfan and dichlofluanid, and thus they were considered

not quantified. Dichlofluanid, however, presented good performance at a level of 0.125 mg kg 1, which is also the last point on its calibration curve and, thus, was also considered not quantified.

Estimation of measurement uncertainty Estimation of measurement uncertainty was based on a combination of ‘‘top-down’’ and ‘‘bottom-up’’

Food Additives and Contaminants

673

Table 3. Average recovery, reproducibility, LOD and LOQ obtained for pesticides analysed by LC–MS/MS (ESI positive mode). 0.01 mg kg 1 0.05 mg kg 1 0.075 mg kg 1 0.1 mg kg 1 0.125 mg kg 1

Pesticide 3-Hydroxycarbofuran Acetamiprid Aldicarb Aldicarb sulfone Azinphos ethyl Azinphos methyl Azoxystrobin BF 500-3 Bifentrine Bioallethrin Boscalid Carbendazin Carbofuran Carbophenothion Carbosulfan Chlorpyrifos Clorfenvinphos Cyazofamid Cyproconazole Cyprodinil Deltamethrin Diazinon Dichlofluanid Difenoconazole Dimethoate Ethion Ethoprophos Ethoxysulfuron Ethyl parathion Etrimfos Fenamidone Fenamiphos Fenamiphos sulfone Fenamiphos sulfoxide Fenarimol Fenhexamid Fenvalerate Fipronil Fluazifop p-butyl Flutriafol Foramsulfuron Furathiocarb Hexaconazole Hexythiazox Imazalil Imidacloprid Indoxacarb Iprodione Iprovalicarb Isoproturon Kresoxim-methyl Linuron Malathion Metalaxyl Methidathion Methomyl Metsulfuron methyl Monocrotophos Myclobutanil

Linear calibration range (mg kg 1) 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.02 0.0075 0.0075 0.0075 0.02 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.02 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.02 0.0075 0.0075 0.0075 0.0075 0.0075 0.02 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.02 0.0075 0.0075 0.0075 0.02 0.02 0.0075 0.0075 0.0075 0.02 0.0075 0.0075

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.125 0.10 0.10 0.10 0.125 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.125 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.125 0.10 0.10 0.10 0.10 0.10 0.125 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.125 0.10 0.10 0.10 0.125 0.125 0.10 0.10 0.10 0.125 0.10 0.10

Rec. (%)

RSD (%)

Rec. (%)

RSD (%)

Rec. (%)

RSD (%)

91 89 98 95 90 85 84 85 88 – 80 85 92 – 73 84 87 87 91 89 89 92 – 85 86 94 92 79 103 89 – 95 90 84 85 86 – 101 92 96 83 87 87 89 85 95 89 – 87 91 91 – – 87 85 85 – 88 92

13 17 13 19 14 11 12 10 14 – 14 17 11 – 24 15 10 12 17 12 17 13 – 10 13 11 12 14 19 12 – 12 11 10 17 18 – 17 12 13 19 8 15 11 9 18 14 – 10 9 14 – – 11 15 11 – 9 14

94 95 94 90 94 89 93 91 90 92 92 88 98 86 77 91 94 94 95 87 88 88 91 88 94 93 92 87 92 90 91 95 90 91 95 87 95 92 92 101 100 92 91 89 92 96 91 88 91 91 94 90 92 91 91 91 91 89 93

10 12 16 9 13 11 15 10 10 12 11 12 8 15 25 11 9 10 11 9 12 10 27 9 10 8 12 18 18 14 14 8 13 14 12 12 16 15 8 18 19 9 10 7 8 10 10 24 10 12 10 11 8 8 12 10 19 11 11

92 92 92 91 92 90 89 89 91 93 93 91 99 90 69 88 95 93 95 90 89 88 85 86 89 91 89 85 96 92 85 94 94 93 90 88 87 92 94 100 106 92 91 90 92 96 88 88 92 93 93 90 92 91 91 91 87 91 91

9 10 16 11 9 10 13 10 9 11 9 12 10 16 26 11 7 9 9 8 12 12 23 9 11 9 8 18 20 8 10 8 10 10 13 11 11 12 7 15 17 7 10 5 11 11 11 19 8 8 11 10 9 9 8 8 19 10 10

Rec. RSD (%) (%)

Rec. (%)

91 89 90 93 94 92 88 89 90 95 94 93 98 90 68 89 95 94 97 92 91 90 88 87 90 93 90 86 100 92 89 94 90 93 97 90 92 97 92 105 106 93 93 89 92 94 91 91 91 92 94 92 93 90 92 91 98 92 92

– – – – – – – – – 96 – – – 94 – – – – – – – – 93 – – – – – – – 89 – – – – – 93 – – – – – – – – – – 100 – – – 97 93 – – – 84 – –

9 11 14 10 13 10 11 7 8 10 12 12 9 13 23 8 8 9 10 9 11 10 24 8 12 10 10 19 17 6 14 8 7 13 10 11 15 12 8 14 14 7 6 10 7 9 12 15 8 7 11 13 11 7 10 8 20 12 11

RSD LOD LOQ (%) (mg kg 1) (mg kg 1) – – – – – – – – – 10 – – – 15 – – – – – – – – 8 – – – – – – – 11 – – – – – 11 – – – – – – – – – – 19 – – – 11 9 – – – 19 – –

0.002 0.004 0.003 0.002 0.004 0.001 0.002 0.003 0.002 0.003 0.003 0.003 0.002 0.01 0.003 0.003 0.002 0.002 0.003 0.003 0.003 0.003 0.02 0.002 0.002 0.002 0.002 0.005 0.003 0.003 0.006 0.003 0.001 0.002 0.003 0.003 0.001 0.005 0.002 0.002 0.005 0.003 0.002 0.002 0.002 0.004 0.003 0.01 0.002 0.002 0.002 0.01 0.006 0.002 0.001 0.003 0.01 0.002 0.002

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.01 0.01 0.01 0.05 nq 0.01 0.01 0.01 0.01 0.01 0.01 0.01 nq 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.075 0.01 0.01 0.01 0.05 0.05 0.01 0.01 0.01 0.05 0.01 0.01

(continued )

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Table 3. Continued. 0.01 mg kg 1 0.05 mg kg 1 0.075 mg kg 1 0.1 mg kg 1 0.125 mg kg 1

Pesticide Omethoate Oxamyl Penconazol Pendimethalin Permethrin cis trans Phenthoate Phorate Phosalone Phosmet Picolinafen Pirimicarb Pirimiphos-ethyl Prochloraz Profenofos Propiconazole Propoxur Pyraclostrobin Pyrazophos Pyridaben Pyridate Pyrimethanil Spiroxamine Tebuconazole Tebufenozide Thiabendazole Thiacloprid Thiamethoxam Triasulfuron Triazophos Trichlorfon Trifloxystrobin

Linear calibration range (mg kg 1) 0.0075 0.0075 0.0075 0.0075 0.02 0.0075 0.02 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.02 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.02 0.0075

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

0.10 0.10 0.10 0.10 0.125 0.10 0.125 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.125 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.125 0.10

Rec. (%)

RSD (%)

Rec. (%)

RSD (%)

Rec. (%)

RSD (%)

83 89 92 87 – 89 – 90 86 90 94 85 84 90 92 – 85 87 88 85 85 90 88 87 81 88 91 95 87 – 90

10 10 14 11 – 15 – 13 15 11 8 9 17 9 11 – 11 13 9 7 18 6 8 13 12 12 13 17 9 – 10

86 93 88 88 85 91 93 92 92 91 93 91 93 90 92 95 90 87 89 87 93 93 91 83 92 96 93 96 92 95 93

12 9 10 8 9 10 17 9 9 8 9 8 12 8 10 8 9 11 9 8 16 7 10 10 14 10 15 14 9 10 9

89 95 89 89 88 90 91 94 90 93 92 90 87 90 91 93 90 88 89 89 92 93 94 94 95 95 99 95 88 93 92

11 11 8 7 9 5 12 11 10 7 11 6 9 7 9 10 9 11 8 8 13 6 8 10 14 13 15 14 8 10 9

Rec. RSD (%) (%) 89 93 93 90 88 93 96 94 88 93 94 91 94 91 93 94 91 86 91 87 93 94 94 94 97 92 95 94 91 94 93

12 11 10 7 9 10 15 9 10 10 9 7 8 9 9 9 9 12 7 7 13 6 9 10 13 13 15 13 7 12 8

Rec. (%) – – – – 92 – 91 – – – – – – – – 95 – – – – – – – – – – – – – 92 –

RSD LOD LOQ (%) (mg kg 1) (mg kg 1) – – – – 9 – 14 – – – – – – – – 11 – – – – – – – – – – – – – 9 –

0.002 0.003 0.002 0.003 0.007 0.002 0.01 0.002 0.001 0.002 0.002 0.002 0.003 0.003 0.002 0.006 0.002 0.003 0.002 0.002 0.006 0.001 0.002 0.002 0.002 0.002 0.005 0.003 0.002 0.01 0.003

0.01 0.01 0.01 0.01 0.05 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.01

Note: n.q., not quantified.

approaches, as described in Eurachem Guide (Eurachem 2000). The weighing of samples, the measurements of volume of the extraction solvent added to the sample, the calibration curve and intralaboratory reproducibility were the principal uncertainty sources associated with the method. It was noted that the uncertainty relating to measurement of volume and weighing of the samples were negligible in comparison with the other uncertainty sources. Since the calibration curve comprises the uncertainty of various steps, such as weighing of standards, extraction procedure, instrumental analysis and statistical treatment of data, the main contribution to method uncertainty arises from construction of the calibration curves for most of the validated pesticides. The percentage expanded uncertainty (U%) for each pesticide, was determined at every spiking level for which repeatability and reproducibility studies were carried out, is showed in Table 4. For the compounds that presented calibration curve between 0.0075 and

0.10 mg kg 1, the uncertainty was calculated at levels of 0.01, 0.05, 0.075 and 0.10 mg kg 1. For the analytes whose calibration curves were set from 0.02 to 0.125 mg kg 1, the uncertainty was assessed at spiking levels of 0.05, 0.075, 0.10 and 0.125 mg kg 1. As can be seen from Table 4, the expanded uncertainty is typically less than 35% and, thus, agrees with Sanco requirements which establishes a default expanded uncertainty value of 50%. However, for some of the pesticides studied, there are uncertainty values at 50%, which can be considered high. For such pesticides, a greater dispersion in analytical results was observed from validation assays, leading to calibration curves in which the prediction limits cover a broader range of instrumental responses and, therefore, a higher variability. The compounds that present smaller uncertainty values generated instrumental response with a lower variability and, thus, calibration curves with prediction limits covering a narrower range. Figure 4 depicts calibration curves for the two

Food Additives and Contaminants Table 4. Expanded measurement uncertainties for pesticides analysed by LC–MS/MS (ESI positive mode) at each spiking level. Pesticide 3-Hydroxycarbofuran Acetamiprid Aldicarb Aldicarb sulfone Azinphos ethyl Azinphos methyl Azoxystrobin BF 500–3 Bifentrine Bioallethrin Boscalid Carbendazin Carbofuran Carbophenothion Chlorpyrifos Clorfenvinphos Cyazofamid Cyproconazole Cyprodinil Deltamethrin Diazinon Difenoconazole Dimethoate Ethion Ethoprophos Ethoxysulfuron ethyl parathion Etrimfos Fenamidone Fenamiphos Fenamiphos sulfone Fenamiphos sulfoxide Fenarimol Fenhexamid Fenvalerate Fipronil Fluazifop p-butyl Flutriafol Foramsulfuron Furathiocarb Hexaconazole Hexythiazox Imazalil Imidacloprid Indoxacarb Iprodione Iprovalicarb Isoproturon Kresoxim-methyl Linuron Malathion metalaxyl Methidathion Methomyl Metsulfuron methyl Monocrotophos Myclobutanil Omethoate Oxamyl Penconazol Pendimethalin Permethrin cis trans

0.01 mg kg 1

0.05 mg kg 1

0.075 mg kg 1

0.10 mg kg 1

0.125 mg kg 1

25 17 13 19 14 11 12 25 14 – 30 23 22 – 28 24 21 22 27 33 31 19 26 17 27 36 53 23 – 18 19 22 36 28 – 45 17 26 49 16 31 20 20 30 24 – 16 18 28 – – 11 19 22 – 23 26 10 30 23 18 –

8 12 16 9 13 11 15 9 10 17 10 9 10 25 9 8 7 8 9 10 9 7 9 6 10 13 18 9 21 7 8 8 12 10 32 14 6 11 16 6 10 6 7 11 8 32 7 8 9 17 13 8 8 8 22 8 9 12 10 8 7 14

8 10 16 11 9 10 13 8 9 20 10 10 12 24 10 8 8 8 9 8 9 8 10 6 10 13 17 9 29 6 9 7 12 11 30 13 5 11 17 6 10 6 8 12 8 32 7 7 8 18 15 9 8 9 32 9 9 11 11 8 7 18

12 11 14 10 13 10 11 13 8 17 21 27 20 20 19 17 16 19 18 15 16 17 21 10 23 21 46 20 27 13 22 13 21 29 26 23 10 22 27 13 19 14 17 31 15 27 17 17 14 16 14 7 24 20 33 19 16 12 23 18 18 17

– – – – – – – – – 23 – – – 17 – – – – – – – – – – – – – – 38 – – – – – 28 – – – – – – – – – – 33 – – – 18 18 – – – 51 – – – – – – 22 (continued )

675

676

F.D. Madureira et al. Table 4. Continued. Pesticide Phenthoate Phorate Phosalone Phosmet Picolinafen Pirimicarb Pirimiphos-ethyl Prochloraz Profenofos Propiconazole Propoxur Pyraclostrobin Pyrazophos Pyridaben Pyridate Pyrimethanil Spiroxamine Tebuconazole Tebufenozide Thiabendazole Thiacloprid Thiamethoxam Triasulfuron Triazophos Trichlorfon Trifloxystrobin

0.01 mg kg 1

0.05 mg kg 1

0.075 mg kg 1

0.10 mg kg 1

0.125 mg kg 1

20 – 22 23 20 18 11 26 20 21 – 20 21 15 16 27 14 22 22 29 25 18 34 19 – 17

8 36 8 7 7 7 5 10 7 8 12 7 8 6 6 9 5 7 8 10 9 9 12 7 21 6

8 37 9 8 7 8 6 9 7 8 14 8 8 6 6 7 5 7 8 11 10 9 14 7 22 7

20 31 20 16 16 16 17 15 17 17 13 17 16 13 14 7 8 13 17 22 21 22 32 17 19 14

– 42 – – – – – – – – 17 – – – – – – – – – – – – – 22 –

types of behaviour discussed. The differences may be due to the physicochemical characteristics of the analytes, such as interaction with the matrix, pesticide distribution in the partition, stability, ionisation behaviour, chromatographic profile among others.

Application to real samples: participation in proficiency tests The method was applied to the analysis of samples from distinct proficiency tests. All the samples received were submitted to analysis via LC–MS/MS using the method presented to identify and quantify all possible pesticides within the scope of the laboratory. The samples analysed included tomato pureé, apple pureé, grape pureé and leek homogenate. To analyse each of these samples, a matrix-matched calibration curve was prepared with a blank extract of the corresponding commodity. No quantification was carried out using a curve prepared with an extract obtained from a commodity different from the sample being analysed. No false negative and no false positive results were reported and the z-scores for the analytes identified demonstrated satisfactory analytical performance. The method was previously validated within a smaller scope of analytes using papaya as a representative matrix for commodities with a high

water content. The analytical performance for the analytes validated at that time was also satisfactory, demonstrating that the method is also suitable for papaya samples. However, further experiments are required to check the performance for the new analytes included in the present method and which were not included at the time the method was validated with papaya. It is also important to implement studies to check whether the method is suitable for other high water content products, which must involve recovery determinations using spiked blank samples of the commodity of interest. This method will be used in routine analysis of official samples from the national pesticide residues monitoring program that comprises other high water content matrices, such as apples, lettuces, bananas, melons, mangoes, sweet peppers, grapes and potatoes, in addition to papayas and tomatoes.

Conclusions A fast, selective and efficient multi-residue method for the determination of pesticide residues in a high water content matrix (tomato) was developed and validated using liquid chromatography–tandem mass spectrometry with electrospray ionisation in positive mode. Evaluation of the significance of regression of the

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(complementary cooperation agreement No. ALA/BRA/ 2004/006-189) for funding this project. The Brazilian agency CNPq (National Council of Scientific and Technological Development) is acknowledged for scholarship support.

References

Figure 4. Comparison between prediction limits obtained for calibration curves of (a) iprovalicarbe and (b) thiamethoxam, showing how response variability might influence the uncertainty measurement, which was greater for thiamethoxam than for iprovalicarbe.

calibration curves through t-tests on the correlation coefficients and analysis of variance of residuals of the linear fit also showed that this is a valuable statistical approach in the validation of multi-residue methods, instead of just examining whether the determination coefficients are 40.99. The satisfactory method performance characteristics achieved for 88 of the 90 pesticides studied shows the validity of the statistical approach. During routine application in monitoring analyses of high water content samples, the performance characteristics will be continuously checked to ensure the robustness of the method. Appropriate quality control samples (such as spiked blank samples and/or reference material) will be run along with routine analysis samples to check whether the performance characteristics of the method remain comparable to those obtained in validation.

Acknowledgements We thank the Ministry of Agriculture, Livestock and Food Supply of Brazil and the European Commission

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Kme´llar B, Fodor P, Pareja L, Ferrer C, Martı´ nez-Uroz MA, Valverde A, Fernandez-Alba AR. 2008. Validation and uncertainty study of a comprehensive list of 160 pesticide residues in multi-class vegetables by liquid chromatography–tandem mass spectrometry. J Chromatogr A. 1215:37–50. Lee SJ, Park HJ, Kim W, Jin JS, El-Aty AMA, Shim JH, Shin SC. 2008. Multiresidue analysis of 47 pesticides in cooked wheat flour and polished rice by liquid chromatography with tandem mass spectrometry. Biomed Chromatogr. 23:434–442. Mauricio AQ, Lins E, Alvarenga MB. 2009. A national residue control plan from the analytical perspective – The Brazilian case. Anal Chim Acta. 637:333–336. Meyer PC, Zu¨nd RE. 2000. Statistical methods in analytical chemistry. 2nd ed. New York: Wiley. Mol HGJ, Plaza-Bolan˜os P, Zomer P, de Rijk TC, Stolker AAM, Mulder PPJ. 2008. Towards a generic extraction method for simultaneous determination of pesticides, mycotoxins, plant toxins, and veterinary drugs in feed and food matrixes. Anal Chem. 80:9450–9459. Pizzutti IR, de Kok A, Hiemstra M, Wickert C, Prestes OD. 2009. Method validation and comparison of acetonitrile and acetone extraction for the analysis of 169 pesticides in soya grain by liquid chromatography–tandem mass spectrometry. J Chromatogr A. 1216:4539–4552. Pizzutti IR, de Kok A, Zanella R, Adaime MB, Hiemstra M, Wickert C, Prestes OD. 2007. Method validation for the analysis of 169 pesticides in soya grain, without clean up, by liquid chromatography–tandem mass spectrometry using positive and negative electrospray ionization. J Chromatogr A. 1142:123–136.

Prestes OD, Friggi CA, Adaime MB, Zanella R. 2009. Quechers – a modern sample preparation method for pesticide multiresidue determination in food by chromatographic methods coupled to mass spectrometry. Quı´ mica Nova. 32:1620–1634. Quackenbush R, Hackley B, Dixon J. 2006. Screening for pesticide exposure: A case study. J Midwif Women’s Health. 51:3–11. Romero-Gonza´lez R, Frenich AG, Martı´ nez-Vidal JL. 2008. Multiresidue method for fast determination of pesticides in fruit juices by ultra performance liquid chromatography coupled to tandem mass spectrometry. Talanta. 76:211–225. Souza SVC, Junqueira RGAJ. 2005. Procedure to assess linearity by ordinary least squares method. Anal Chim Acta. 552:25–35. Stubbings G, Bigwood T. 2009. The development and validation of a multiclass liquid chromatography tandem mass spectrometry (LC–MS/MS) procedure for the determination of veterinary drug residues in animal tissue using a QuEChERS (QUick, Easy, Cheap, Effective, Rugged and Safe) approach. Anal Chim Acta. 637:68–78. Tamura M, Uyama A, Mochizuki N. 2011. Development of a multi-mycotoxin analysis in beer-based drinks by a modified QuEChERS method and ultra-high-performance liquid chromatography coupled with tandem mass spectrometry. Anal Sci. 27:629–635. Venkateswarlu P, Mohan KR, Kumar CR, Seshaiah K. 2007. Monitoring of multi-class pesticide residues in fresh grape samples using liquid chromatography with electrospray tandem mass spectrometry. Food Chem. 105:1760–1766.

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 679–693

Modelling uncertainty estimation for the determination of aflatoxin M1 in milk by visual and densitometric thin-layer chromatography with immunoaffinity column clean-up K.L. Carvalhoa, G.A.A. Gonc¸alvesa, A.L. Lopesa, E.A. Santosa, E.A. Vargasa and W.F. Magalha˜esb* a

Laboratory of Quality Control and Safety Food – LACQSA/LANAGRO-MG/MAPA, Av. Raja Gabaglia, 245, Cidade Jardim, CEP 30380-090 – Belo Horizonte, MG, Brazil; bChemistry Department, Universidade Federal de Minas Gerais – UFMG, Av. Pres. Antonio Carlos, 6627, Campus Pampulha, CEP 31270-901 – Belo Horizonte, MG, Brazil (Received 26 November 2010; final version received 7 November 2011) The uncertainty of aflatoxin M1 concentration in milk, determined by thin-layer chromatography (TLC) with visual and densitometric quantification of the fluorescence intensities of the spots, was estimated using the causeand-effect approach proposed by ISO GUM (Guide to the expression of uncertainty in measurement) following its main four steps. The sources of uncertainties due to volume measurements, visual and densitometric TLC calibration curve, allowed range for recovery variation and intermediary precision to be taken into account in the uncertainty budget. For volume measurements the sources of uncertainties due to calibration, resolution, laboratory temperature variation and repeatability were considered. For the quantification by visual readings of the intensity of the aflatoxin M1 in the TLC the uncertainty arising from resolution calibration curves was modelled based on the intervals of concentrations between pairs of the calibration standard solutions. The uncertainty of the densitometric TLC quantification arising from the calibration curve was obtained by weighted least square (WLS) regression. Finally, the repeatability uncertainty of the densitometric peak areas or of the visual readings for the test sample solutions was considered. For the test samples with aflatoxin M1 concentration between 0.02 and 0.5 mg l 1, the relative expanded uncertainties, with approximately 95% of coverage probability, obtained for visual TLC readings were between 60% and 130% of the values predicted by the Horwitz model. For the densitometric TLC determination they were about 20% lower. The main sources of uncertainties in both visual and densitometric TLC quantification were the intermediary precision, calibration curve and recovery. The main source of uncertainty in the calibration curve in the visual TLC analysis was due to the resolution of the visual readings, whereas in the densitometric analysis it was due to the peak areas of test sample solutions followed by the intercept and slope uncertainties of the calibration line. Keywords: chromatographic analysis; clean-up – affinity columns; statistical analysis; regression; measurement uncertainty; aflatoxins; mycotoxins – aflatoxins; mycotoxins; milk

Introduction One of the most important metrological characteristics of a measurement result is its uncertainty. It is a worldwide consensus that a result is not complete without an expression of its uncertainty (ABNT, INMETRO 2003; BIPM et al. 1995) and its estimation is a requirement for testing laboratories accreditation by the International Organization for Standardization (ISO) (2005). The present paper uses the methodology called the cause-and-effect approach (Ellison and Barwick 1998a, 1998b; Barwick and Ellison 1998), or GUM approach (ABNT, INMETRO 2003; BIPM et al. 1995) or bottom-up approach to estimate the uncertainty of aflatoxin M1 concentration in bovine milk which was

*Corresponding author. Email: welmag@ufmg.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.648959 http://www.tandfonline.com

determined by thin-layer chromatography (TLC) with visual and densitometric quantification. Within this methodology, this work addresses especially to the following issues: . Evaluation of the uncertainty due to the resolution of the intensity of aflatoxin M1 in the TLC quantification by a visual reading. . Accounting for the heteroskedasticity of the instrumental response (fluorescence intensity) in the densitometric TLC calibration by using weighted least squares (WLS). . Inclusion of an uncertainty contribution due to the uncorrected bias within an allowed range for the recovery variation.

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K.L. Carvalho et al. . Accounting for a within-laboratory reproducibility or intermediary precision through the data of the analysis of control samples for quality control.

Other approaches that estimate the uncertainty of an analytical measurement make use of data obtained from a method validation study (Ellison et al. 2000; Barwick and Ellison 2000a, 2000b; Barwick et al. 2000) or from interlaboratory studies through the results of collaborative trials (Adriaan et al. 1998; Barwick and Ellison 1998; Ellison 1998). Recent publications have addressed considerations to assess the uncertainty of aflatoxin M1 determination (Calaresu et al. 2006; Populaire and Gime´nez 2006). Calaresu et al. (2006) used data obtained from precision, trueness and ruggedness studies during method validation to estimate uncertainty components. However, the uncertainties due to the input quantities appearing in their measurand equation and in their cause-and-effect diagram were not included in their uncertainty budget. For instance, glassware tolerances were used to estimate uncertainties and not the data from the calibration certificates and laboratory temperature variation which appear in the cause-and-effect diagram. A very simple measurand equation was presented where only three input quantities appear: the volume of the sample taken for analysis, the final volume of the sample solution, and the concentration of aflatoxin evaluated from the calibration curve. However, nothing was presented about the least square procedure used to fit the calibration curve data, nor about both the calibration curve range or the values of the intercept, the slope of the calibration curve, and their uncertainties and covariance. Populaire and Gime´nez (2006) compared the uncertainty estimation obtained from the bottom-up method with the top-down method. They concluded that the main sources of uncertainties in different analytical methods were mainly due to the intermediary precision, accuracy/recovery and, in some cases, calibration. They also concluded that the uncertainties due to weighting and volume measurement were in general negligible. However, no details of their calculations were given, also they used a poor calibration design with only one standard calibration solution. The present paper presents a detailed uncertainty budget to estimate the measurement uncertainty for aflatoxin M1 determination in milk with immunoaffinity column clean-up and thin-layer chromatographic (TLC) determination with visual and densitometric quantification. Aflatoxin M1 (4-hydroxyaflatoxin B1, (6aR-cis)2,3,6a,9a-tetrahydro-9a-hydroxy-4-methoxycyclopenta [c]furo[30 ,20 :4,5]furo[2,3-h][l]benzopyran-1,11-dione) is a genotoxic carcinogenic hydroxylated metabolite of aflatoxin B1 found in the milk of animals that have

consumed feedstuffs contaminated with aflatoxin B1. Recent reports of the International Agency for Research on Cancer (IARC) assessed the potential carcinogenic risks to humans (World Health Organization (WHO) and IARC 2002) of aflatoxin M1, and concluded that even very low levels of exposure to aflatoxins, i.e. 1 ng kg 1 bw day 1 or less contribute to a risk of liver cancer (Byrne 2000). Due it high human risks the European Union has established a very restrict maximum residue limit (MRL) or maximum permitted limit (MPL) for aflatoxin M1 content in milk at 0.050 mg kg 1 (Commission of the European Communities 1998, 2001, 2004, 2010). European Union legislation (Commission of the European Communities 2006) states that the uncertainty of the analytical measurement for aflatoxin M1 contamination should be considered when reporting and interpreting the analytical results (Commission of the European Communities 2002, 2004, 2006). Besides, European Union legislation (Commission of the European Communities 2006) establishes the necessity of declaring the correction of the aflatoxin M1 contamination by the recovery, or not, and what is the recovery. It also establishes the minimum performance criteria that an analysis procedure should meet to be used in the analysis of aflatoxin M1 in milk that was used when setting criteria for method performance. MERCOSUR and Brazil (Ageˆncia Nacional de Vigilaˆncia Sanita´ria (ANVISA) 2011; MERCOSUR/ GMC/RES 2002) have established limits of 0.5 and 5 mg kg 1 for aflatoxin M1 in milk and powdered milk along sampling plans (Mercosur), but no method performance has been established, nor the necessity of reporting the recovery or uncertainty of the analytical measurement. Recovery and measurement uncertainty have been reported when demanded by customers or according to institution policies as defined by ISO/IEC 17025 (ISO 2005). The LACQSA/LANAGRO-MG/MAPA laboratory is accredited according ISO/IEC 17025 by the national metrology institute – the Instituto Nacional de Metrologia Normalizac¸a˜o e Qualidade Industrial (INMETRO) – to realise the analytical procedure for the determination of the herein reported ‘determination of aflatoxin M1 in milk by visual and densitometric thin-layer chromatography (TLC) with immunoaffinity column clean-up’.

Materials and methods Uncertainty estimation procedure In the cause-and-effect approach for uncertainty estimation, or bottom-up approach, the detailed knowledge of the chemical analytical procedure is necessary to enable the correct identification of the measurand and of its sources of uncertainties. In this paper the

Food Additives and Contaminants four steps presented elsewhere were strictly followed (Carvalho, Santos, et al. Forthcoming).

Step 1: Measurand specification Measurand specification is realised through the measurand equation and the measurement procedure (ABNT, INMETRO 2003; BIPM et al. 1995; Ellison et al. 2000) or analytical procedure, which is normally documented through the standard operation procedures. The analytical procedure for aflatoxin M1 determination using TLC is summarised below. Complete measurand equations, also presented below, show in detail all the direct measurements realised during the analytical procedure using TLC with densitometric quantification (Equation 2) or with visual quantification (Equation 3).

Analytical procedure The analytical procedure for aflatoxin M1 determination was adapted from a published immunoaffinity clean-up method (Dragacci et al. 2001). It can be summarised in the following steps: (1) A volume of 100 ml (Vs) of defatted milk was taken after centrifugation and filtered by using folded filter paper. (2) Clean-up was done by using an immunoaffinity column (VICAN or R-Biopharm), washing with 5 ml of water, followed by the elution of aflatoxin M1 with a solution of 2.5 ml of acetonitrile and methanol (2:1, v/v) and 2.5 ml of pure methanol. The sample eluate was evaporated at 40 C under nitrogen bubbling just before complete dryness (critical step). (3) The dried extract was re-dissolved with the addition of 100 ml (Vr) of toluene–acetonitrile (9:1 v/v) and by homogenisation with sonication and vortexed mix. (4) Application of 20 ml (Va) of the sample extract obtained in step 3 and of 10 ml (Vp) of the calibration aflatoxin M1 standard solutions (six spots) was made on the TLC plate, followed by elution with ether–methanol–water (96:3:1 v/v/ v). (5) Detection and quantification step: the fluorescence intensities of the spots for the test samples and the calibration solutions were read under ultraviolet light ( ¼ 365 nm) by visual comparison between sample spots and calibration standard solutions spots or by using a densitometer (dual wavelength flying spot scanning densitometer; model CS-9301PC, Shimadzu, Sa˜o Paulo, Brasil) at a wavelength of 365 nm.

681

Step 2: Identifying the sources of uncertainties As proposed elsewhere (Carvalho, Santos, et al. Forthcoming) the cause-and-effect, or Ishikawa, diagram was constructed by considering the input quantities that appear in the measurand equation as the primary sources of uncertainties. Other sources of uncertainties considered are the allowed range of uncorrected bias due to the allowed variation of the recovery ratio (RR), and the intermediary precision or internal reproducibility or intra-laboratory reproducibility obtained from the analysis of control samples for quality control.

Volumes For the measurements of volume, four basic sources of uncertainties were considered: instrumental uncertainty (Joint Committee for Guides in Metrology (JCGM/WG 2) et al. 2008; INMETRO 2009) due to the formal calibration of the instrument of volume measurement; uncertainty due to resolution for graduated volume instruments; uncertainty due to the variation of the laboratory temperature around the mean laboratory temperature; and uncertainty due to volume measurement repeatability.

Calibration For TLC calibration, six standard calibration solutions were used labelled P1, P2, . . . , P6 with concentrations near to 1.0, 0.5, 0.3, 0.2, 0.1 and 0.05 mg ml 1, respectively. As presented by Carvalho, Santos, et al. (Forthcoming), the resolution of the visual calibration was modelled by considering the different intervals (D’s) among the concentrations of the calibration solutions. This model considers that when reading, by visual comparison, the analyst can state that the fluorescence of the sample spot is equal to the fluorescence of one of the calibration standard solutions, Pi, or that its fluorescence is found between two successive solutions Pi–Piþ1. This model is represented in Figure 1. From this model the uncertainty of the visual quantification due to calibration resolution

•••••

Δ(P4-P5)

Δ(P3-P4)

P4 •••••

Δ(P2-P3)

P3 P3P4

•••••

Δ(P1-P2)

P2P3

Δ(P2P3-P3P4)

P1 P1P2

Δ(P1P2-P2P3)

Figure 1. Variations (D) between any pair Pi–Piþ1 of standards and between half readings PiPiþ1–Piþ1Piþ2 used to model the uncertainty due to the resolution of the visual calibration of the fluorescence readings in the TLC.

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(uVCresol) was estimated for different fluorescence readings as a function of the standard calibration solution concentration, C(Pi) by using the rectangular probability density function (PDF) of the following equations (1a) and (1b): CðPi Þ CðPiþ1 Þ CðPi Þ þ CðPiþ1 Þ pffiffiffi uVCresol ¼ ð1aÞ 2 2 3 The uncertainty obtained by Equation (1a) is attributed to the averaged concentration of the standard solutions Pi and Pi þ 1: ½CðPi Þ þ CðPi þ1Þ =2 CðPi Þ þ CðPiþ1 Þ CðPiþ1 Þ þ CðPiþ2 Þ uVCresol þ 2 2 2 CðPi ÞþCðPiþ1 Þ CðPiþ1 ÞþCðPiþ2 Þ 2 2 pffiffiffi ð1bÞ ¼ 2 3 The uncertainty obtained by Equation (1b) is attributed to the concentration:

underestimation of this source of uncertainty. This model does not consider the intrinsic Poisson uncertainty of the densitometric peak area of each individual spot, which is negligible compared with the other two sources of uncertainties considered. Recovery The analytical method presents a recovery ratio variable from day to day, but no correction is applied to the results. Therefore, the allowed recovery ratio range was used to estimate a Type B uncertainty, as described by Carvalho, Santos, et al. (Forthcoming). Intermediary precision Although the uncertainty of repeatability was included in the volume and calibration uncertainty sources, it also included an intermediary precision to account for other sources of uncertainties from bath-to-bath, dayto-day, sample preparation-to-sample preparation, etc. variations.

f½CðPi Þ þ CðPiþ1 Þ =2 þ ½CðPiþ1 Þ þ CðPiþ2 Þ =2g=2 For each visual reading, the modelled resolution uncertainty was combined with the reproducibility standard deviation due to the two or three readings of the TLC spots fluorescence intensities of different analysts. For the densitometric TLC calibration, the areas of the calibration peaks, associated with the fluorescence of each standard calibration solution spots on the TLC plate, were fitted against the standard calibration solution concentration using the weighted least square (WLS). To estimate the uncertainties on the densitometric TLC peak areas, the results of the calibration curves obtained over 8 months were used. The areas of the densitometric peaks of each of four sets of calibration standard solutions were measured on at least 3 days to obtain a model for the sample standard deviations (square root of the variances) of these areas as a function of the standard calibration solution concentration. This model was used to weight each densitometric peak area in the WLS fitting of the calibration curves (see the last column of Table 2). With this approach, the uncertainties of the intercept and slope of the calibration curves take into account the sources of uncertainties due to the intermediary precision of the densitometric peak areas as well as, and implicitly, that due to the preparation of the standard calibration solutions, as required by European Union norms (Commission of the European Communities 2002, 2004). Although these European laws do not specifically apply to mycotoxin analysis, the use of the above approach to estimate calibration uncertainty is consistent with statistical concepts and is recommended in order to avoid the

Step 3: Estimating (quantifying) the standard uncertainties of each source of uncertainty The uncertainties due to volume measurements, visual and densitometric TLC calibration and quantification were estimated as described by Carvalho, Santos, et al. (Forthcoming). Recovery When recovery is not used to correct the results, its uncertainty is obtained from the allowed variation of the recovery of the quality control samples analysed with each bath of analysis. For the samples with aflatoxin M1 contamination from 0.01 to 0.05 mg l 1, the allowed recovery ratio range is from 60% to 120%; above this contamination this allowed range becomes from 70% to 110% (Commission of the European Communities 2006). Each of these ranges defines a total amplitude 2a of the rectangular PDF for a Type B estimation of the uncertainty. Intermediary precision The uncertainties of intermediary precision of the densitometric and visual TLC methods were obtained from the straight line model fitted to the data of the standard deviation of the results of analysis of the fortified blank samples with 0.02, 0.05 and 0.5 mg l 1 for quality control, which was carried out over a period of 11 months. This is a Type A estimation; and as only three points were used to fit the straight line, the degrees of freedom of this uncertainty component was only 1.

Food Additives and Contaminants Step 4: Calculating the combined and expanded uncertainty of the measurand To calculate the combined standard uncertainty of the concentration of aflatoxin M1 in the test sample, the classical uncertainties propagation law was used. To obtain the expanded combined uncertainty, the standard combined uncertaity is multiplied by the coverage factor to a coverage probability of 95%, as described by Carvalho, Santos, et al. (Forthcoming). The coverage factor is obtained from the student PDF point of probabilities, depending on the effective degree of freedom calculated by the Welch-Satterthwaite equation (INMETRO 2009; Joint Committee for Guides in Metrology (JCGM/WG 2) 2008; Carvalho, Santos, et al. Forthcoming).

recovery; RR is the recovery ratio measured through the analyses of a spike sample of quality control during the bath of analysis; CResol is a null correction applied to the average reading LVm due to the resolution of the visual calibration (by introducing this null correction the not null uncertainty due to the visual reading resolution was computed); Cprecint is the null correction due to the intermediary precision; and LVm is the mean visual reading for the test sample concentration (mg ml 1 or ppm) obtained by the average of three individual readings of three different analysts for the fluorescence intensity of the test sample spot compared with the fluorescence of the six spots of the aflatoxin M1 standard calibration solutions. Note that CSAA is obtained from the intercept and slope of the calibration curve by the equation: CSAA ¼

Results and discussion Step 1: Measurand specification After a detailed analysis of the standard operation procedures the proposed measurand equations were: For densitometric TLC quantification: Vp CSAA Vr CFrecup þ Cprecint Va Vs Vp ðAPDA aÞ Vr 1 þ Cprecint ¼ b Va Vs RR ð2Þ

683

APDA a b

ð4Þ

Therefore, the uncertainty of CSAA, u(CSAA), will be dependent on the uncertainties and covariance of a and b, but also on the repeatability uncertainty of the instrumental response for the test sample solution applied to the TLC plate, u(APDA).

CaflaM1 ¼

For visual TLC quantification: CaflaM1 ¼

Vp ðLVm þ CResol Þ Vr Va Vs CFrecup þ Cprecint

ð3Þ

where CaflaM1 is the aflatoxin M1 concentration (mg l 1 or ppb) quantified in the milk sample; CSAA is the concentration (mg ml 1 or ppm) of the test sample solution applied in the TLC plate; Vp is the volume (ml) of the standard solutions of different concentrations applied to the TLC plate to quantify the aflatoxin contamination by using a densitometer calibration curve or by visual comparison of the fluorescence intensity related to aflatoxin M1 from the standards and the test sample spots; Vr is the volume (ml) of the solvent used to re-dissolve the sample after drying the test sample extract obtained after the sample clean-up through the elution of aflatoxin M1 from the immunoaffinity column; Va is the volume (ml) of the test sample solution applied to the TLC plate; Vs is the test sample volume (ml) of the milk injected onto the immunoaffinity column to be purified; APDA is the area for the densitometric peak for the test sample solution applied in the TLC plate; a and b are, respectively, the intercept and slope of the densitometer calibration curve; CFrecup ¼ 1/RR is the correction factor of the unitary value due to the uncorrected

Step 2: Identifying the sources of uncertainties The cause-and-effect (Ishikawa) diagrams, obtained for densitometric and visual methods, are showed in Figures 2 and 3, respectively. All the input quantities in Equations (2) and (3) are present in these cause-and-effect diagrams. For Figures 2 and 3 some symbols are defined as above; other symbols are as follows: CP, concentration of the calibration standard solutions; APDP, area of the densitometric peak for the spots of the calibration standard solutions; Calibr, calibration; Resol, resolution of the measurement instrument; Repet, repeatability; and Lab Temp Var, variation of the laboratory temperature around its mean value.

Step 3: Estimating (quantifying) the standard uncertainties of each source of uncertainty Volume The three Type B uncertainties of the volume measurements due to the resolution, uResol(V), the calibration, uCalib(V), and the total laboratory temperature variation, DT, of 5 K around the mean laboratory temperature, uVartemp(V), as well as the Type A uncertainty due to the repeatability, uRep(V), were obtained as shown elsewhere (Carvalho, Santos, et al. Forthcoming). The measurement function (INMETRO 2009; Joint Committee for Guides in Metrology (JCGM/WG 2)

684

K.L. Carvalho et al. Calibration APDP

CSAA

Allowed recovery variation range

a b

uc(CaflaM1) and U(CaflaM1)

APDA Purification Resol

Experimeters

Volumes Lab Temp Var

Different days

Calibr Repeat

Intermediary precision or internal reproducibility or intralaboratorial precision

Figure 2. Cause-and-effect (Ishikawa) diagram to represent the sources of uncertainties to estimate the uncertainty of the aflatoxin M1 content u(CaflaM1) for densitometric TLC quantification.

Resolution

Repeatabilty

Allowed recovery variation range

LVm

Calibration

u(CaflaM1) and U(CaflaM1)

Purification Resol

Experimeters Volumes

Lab Temp Var

Different days

Calibr Repeat

Intermediary precision or internal reproducibility or intralaboratorial reproducibility

Figure 3. Cause-and-effect (Ishikawa) diagram to represent the sources of uncertainties to estimate the uncertainty of the aflatoxin M1 content u(CaflaM1) for visual TLC quantification.

2008) or measurand quantity is:

equation

the

volume

V ¼ Vnominal þCResol þ CCalib þ CVartemp þ CRep

ð5Þ

By applying the law of uncertainty propagation on Equation (5) the combined uncertainties of each of the four measured volumes, V (Vs, Vr, Va or Vp), were obtained by the equation: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uðVÞ ¼ u2Resol ðVÞ þ u2Calib ðVÞ þ u2Vartemp ðVÞ þ u2Rep ðVÞ ð6Þ Table 1 shows the metrological characteristics of the instruments used for volume measurement: their standard uncertainties due to instrument resolution, calibration, repeatability and laboratory temperature variation, as well as the combined uncertainty of volume measurement u(V) as calculated by Equation (6). Calibration The uncertainty of the concentration of the aflatoxin M1 test sample solution, u(CSAA), applied to the TLC plate determined by the densitometric quantification

came from the uncertainties u(a) ¼ sa and u(b) ¼ sb, and from the covariance u(a,b) ¼ cov(a, b) of the intercept and of the slope of the calibration straight line (Carvalho, Gonc¸alves, et al. Forthcoming, Carvalho, Santos, et al. Forthcoming). To quantify a test sample, a set of standard calibration solutions with the concentrations presented in Table 2 was used. The same set of calibration standard solutions was used for visual and densitometric TLC quantification. The peak areas of these calibration standard solutions for a typical calibration are also shown in the Table 2. The uncertainties (sample standard deviations) of the APDP in the last column of Table 2 and the uncertainties bars in Figure 4 clearly show the heteroskedasticity of the instrumental responses, and were experimentally obtained as presented above through the linear model: uðAPDPÞ ¼ 10:80037 þ 165:56443 CP

ð7Þ

By fitting a straight line to the data of the Table 2 using the WLS (the continuous line in Figure 4), the intercept a ¼ (7.83 17.00), the slope b ¼ (1767.18

Food Additives and Contaminants

685

Table 1. Metrological characteristics of the instruments used for volume measurements inp TLC ffiffiffi quantification of aflatoxin M1: scale division (d), scale space (SS), kd is the divisor in the equation: uResol(V) ¼ SS/[kd x 3], uncertainties due to resolution, calibration laboratory temperature variation and repeatability, as well as the combined standard uncertainty for graduated cylinder (Vs), automatic pipette (Vr) and microsyringe (Va and Vp). Quantity

Value

uResol (V)

uCalib (V)

uVartemp (V)

uRep (V)

u (V)

Vs Vr Va Vp

100 ml 100 ml 20 ml 10 ml

1 ml 1 ml 0.5 ml 0.5 ml

1.6 mm – 1.5 mm 1.5 mm

4 4 4

0.14 ml 0.29 ml 0.072 ml 0.072 ml

0.51 ml 0.26 ml 0.085 ml 0.080 ml

0.030 ml 0.030 ml 0.0061 ml 0.0030 ml

0.31 ml 0.001 ml 0.001 ml 0.001 ml

0.61 ml 0.39 ml 0.11 ml 0.11 ml

Table 2. Calibration data of the TLC densitometric quantification. Standard P6 P5 P4 P3 P2 P1

CP (mg ml 1)

APDP

u (APDP)

0.036802 0.101913 0.184010 0.331218 0.496827 0.993655

71.019 188.972 342.176 584.259 920.293 1696.175

16.893 27.674 41.266 65.638 93.057 175.314

Note: CP, concentration of the calibration standard solution; APDP, area of the densitometric peak for the spots of the calibration standard solutions.

Figure 4. Calibration curve of the densitometric TLC quantification as obtained by WLS (continuous line) and OLS fitting (dotted-dashed line). UPL and LPL are the upper and the lower prediction limit curves for 95% of confidence.

115.91) ml mg 1 and the covariance between the intercept and the slope cov(a, b) ¼ 1248.1 ml mg 1 were obtained. The intercept standard uncertainty of 17 ml mg 1 shows that it is statistically equal to zero. For comparison, the intercept and the slope of the calibration line obtained from the ordinary least square (OLS) fit (dash and dotted line in Figure 4) were a ¼ 24.73 and b ¼ 1704.2 ml mg 1. The uncertainties of these OLS fitted parameters were not reported because they do not have any statistical meaning once the residual standard deviation of the OLS fit does not

represent the uncertainty of any instrumental response within the calibration concentration range. The WLS and OLS lines cross each other at the crossing point (0.268 mg ml 1) marked in the Figure 4 with a signal plus (þ). At concentrations lower than 0.268 mg ml 1, before the crossing point in Figure 4, the WLS line is below the OLS line; after this concentration the WLS line is above. This implies that the use of the OLS fit in the present case generates a proportional negative bias for milk samples contaminated with aflatoxin M1 at levels of contamination lower than 0.268/ 2 ¼ 0.134 mg ml 1 and a positive bias above this contamination. Figure 4 also shows the upper, UPL (dashed line), and lower (dotted line), LPL, prediction limits (lines) for 95% of confidence as obtained by the WLS. Due the linear heteroskedasticity they are not symmetrical around the centroid (median point, arithmetic average), as occurs in the OLS, but are around the barycentre (weighted centroid, weighted average), where the prediction limit lines are the nearest to the WLS fitted straight line. The region around the barycentre of the calibration line is of better precision. The area of the densitometric peak for a test sample solution was 201.082, leading, according to Equation (4) and the WLS calibration fitting parameters, to CSAA ¼0.1094 mg ml 1 and, according to Equation (2), to an aflatoxin M1 content of 0.05468 mg l 1. This content on the basis of the OLS fit is 5.4% less than the content obtained from the WLS statistical method. The barycentre and the centroid, which are marked in Figure 4, are the points with coordinates (0.0929 mg ml 1, 172) and (0.3574 mg ml 1, 634), respectively. Using the curves of the limit of prediction, as recommended by the standard ISO 11843 (ISO 1997, 2000) and described by Carvalho, Santos, et al. (Forthcoming), the decision limit CC ¼ 0.1171 mg ml 1 (see CC in Figure 4) was obtained, corresponding, according to Equation (2), to aflatoxin M1 contamination CC ¼ 0.059 mg l 1. Using the simplified methodology recommended in European Commission Decision 657 (Commission of the European Communities 2002), a decision limit of CC ¼ 0.1132 mg ml 1 corresponding to an aflatoxin

K.L. Carvalho et al.

M1 contamination of CC ¼ 0.055 mg l 1 was found. Note that these decision limits are nearly 10–20% larger than the MPL of the European Union. However, these calculated CC have only taken into account the calibration curve uncertainty. By including the within-laboratory reproducibility they will become higher. According to European legislation concerning residues in animals and animal products for human consumption, samples with residue levels above the limit of decision will be considered as being noncompliant (Commission of the European Communities 2002, 2004; ISO 1997). Once, even taking into account the measurement uncertainty, such samples exceed the maximum residue limit (MRL) beyond reasonable doubt (Commission of the European Communities 2002, 2004). Nowadays the decision limit is not applied to decide about the compliance of the products contaminated with aflatoxins. However, due to its statistical consistency, it is expected that in the near future the decision limit will also apply to mycotoxins as well as to all other products and contaminants. In the visual quantification the readings of three analysts for the fluorescence intensity of a sample spot were equivalent to those spots of the standard P5, between the standards P5 and P6 (mean concentration of P5 and P6), and the standard P5 again. These readings lead to a mean standard concentration LVm ¼ 0.0911 mg ml 1 and standard deviation of repeatability ¼ 0.0108 mg l 1. This LVm corresponds, according to Equation (3), to an aflatoxin M1 contamination of 0.0455 mg l 1. This contamination, determined by the visual TLC quantification, was 16.7% lower than that of the densitometric TLC quantification. For other contamination levels the differences between these methods is nearly between 15% and 20% and the signal of these differences alternates randomly, and both analytical procedures can be considered to be equally accurate concerning its trueness. The uncertainty of the mean read of the spot fluorescence of the test sample in the visual TLC quantification has two components: repeatability and resolution. The first was obtained as the standard deviation of three readings. To obtain a mathematical model for the uncertainty due to the resolution of the visual TLC calibration according to Figure 1 and the strategy presented by Carvalho, Santos, et al. (Forthcoming), Table 3 was constructed. Figure 5 shows the linear and parabolic (the numerator in Equation 8) functions fitted to the data of DCP against CP of Table 3. Owing to the coefficient of determination, R2, the parabolic function is only slightly better than the linear one. However, the parabolic function fits the data better at low concentrations, near the standard concentration of 0.1 mg ml 1, which correspond to the maximum permitted contamination level of 0.05 mg l 1 (European

Table 3. Modeling the typical uncertainty of resolution of the visual TLC calibration. Standard solution P1 Mean P2 Mean P3 Mean P4 Mean P5 Mean P6

of P1P2 of P2P3 of P3P4 of P4P5 of P5P6

CP (mg ml 1)

DCP

uResol (LVm)

1.000 0.750 0.500 0.400 0.300 0.250 0.200 0.150 0.100 0.075 0.050

– 0.500 0.350 0.200 0.150 0.100 0.100 0.100 0.075 0.050 –

0.787 0.513 0.294 0.222 0.160 0.132 0.106 0.082 0.061 0.051 0.042

0.227 0.148 0.0849 0.0641 0.0462 0.0381 0.0306 0.024 0.018 0.015 0.012

Note: CP, typical standard concentration; DCP, concentration variation between neighbouring calibration solutions; Rr ¼ 2a is the range of the rectangular PDF. In the second column, values shown in bold are the concentrations of real standard calibration solutions; other values correspond to half readings.

Concentration variation between neighbours standard solutions 0.60 0.50 0.40 ΔCP

686

y = 0.672x – 0.023 R² = 0.951

0.30 0.20 y = 0.447x2 + 0.315x + 0.025 R² = 0.969

0.10 0.00 0.00

0.20

0.40

0.60

0.80

CP = Standard Concentration

Figure 5. Linear and the parabolic mathematical functions modelling the aflatoxin M1 concentration variation, DCP, between successive calibration solution concentrations and between successive half calibration solution concentrations (mean between two standards) used to estimate the uncertainty of resolution of the visual TLC calibration.

Commission 2010). The fourth column of Table 3 shows the calculated standard concentration variation around each possible reading of the visual calibration according to the parabolic model, which is used as the range of the rectangular PDF (Rr) to estimate the uncertainty of resolution of the mean visual reading, uResol(LVm): Rr 0:447CP2 þ 0:315CP þ 0:025 pffiffiffi uResol ðLVmÞ ¼ pffiffiffi ¼ ð8Þ 2 3 2 3 where CP is the concentration of a real or hypothetical standard calibration solution at the mean visual reading.

Food Additives and Contaminants

687

For the visual TLC quantification:

Recovery As mentioned above, the uncertainty due to the recovery variation assumes two different values depending on the contamination of the test sample. The correction factor due to the recovery (CFrecup) is equal to the inverse of the recovery ratio. For samples with contamination in the range 0.01–0.05 mg l 1, for which the allowed range of the recovery ratio was 60–120%, the standard uncertainty of the recovery ratio u(RR) was estimated by (Carvalho, Santos, et al. Forthcoming, Carvalho, Gonc¸alves et al. Forthcoming): uðRRÞ ¼

1:20 0:60 pffiffiffi ¼ 0:1732 2 3

ð9Þ

As CFrecup ¼ 1/RR, the uncertainty of the correction factor of recovery u(CFrecup) is (Carvalho, Gonc¸alves et al. Forthcoming, Carvalho, Santos, et al. Forthcoming):

uðRRÞ 1:20 0:60 u CFrecup ¼ ¼ pffiffiffi RR2 2 3 RR2

ð10Þ

While for samples with contamination above 0.05 mg l 1, for which the allowed range of recovery was 70–110%, the standard uncertainty of the recovery ratio u(RR) was estimated by (Carvalho, Gonc¸alves et al. Forthcoming, Carvalho, Santos, et al. Forthcoming) uðRRÞ ¼

1:10 0:70 pffiffiffi ¼ 0:1155 2 3

uðRRÞ 1:10 0:70 ¼ pffiffiffi u CFrecup ¼ RR2 2 3 RR2

ð11Þ

ð12Þ

As the recovery ratio varies significantly even within a batch of analysis, the median value 0.90 of the allowed range of recovery ratio variation was used for both the recovery ratio range in Equationspffiffi(10) ffi and (12), leading to values ofpffiffi0.740741/(2 3) ¼ ffi 0.21383 mg l 1 and of 0.493827/(2 3) ¼ 0.14256 mg l 1 as standard uncertainties for the correction factor of the recovery ratio.

uðprecintÞ ¼ 0:0006 þ 0:2706 CaflaM1ðmg=kgÞ ð14Þ

Step 4: Calculating the combined and expanded uncertainty of the measurand To combine the standard uncertainties of the input and influence quantities, u( ), the particular case of the uncertainty propagation law by propagating the relative uncertainties was not used, as often occurs (Ellison et al. 2000; Barwick and Ellison 2000a, 2000b; Armishaw 2003; Calaresu et al. 2006), because the present measurand equations do not only have multiplications and division operations. For densitometric TLC quantification the uncertainty propagation law takes the form: uðcaflaM1Þ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi i2 h i2 uh u @CaflaM1 @CaflaM1 u ð Vp Þ þ u ð CSAA Þ u @Vp @CSAA u u i2 h i u h u þ @CaflaM1 uðVrÞ þ @CaflaM1 uðVaÞ 2 u @Vr @Va u ¼u h i u u þ @CaflaM1 uðVsÞ 2 u @Vs u u t h

i2 h@CaflaM1

i2 þ @CaflaM1 þ @Cprecint u Cprecint @CFrecup u CFrecup vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uh i2 h i2 u @CaflaM1 @CaflaM1 u ð Vp Þ þ u ð APDA Þ u @Vp @APDA u u u h i2 h i2 u @CaflaM1 uðaÞ þ @CaflaM1 uð bÞ uþ @a @b u u u h i2 h i2 u @CaflaM1 uðVaÞ u þ @Vr uðVrÞ þ @CaflaM1 @Va ¼u u uh i2 u @CaflaM1 u ð Vs Þ u @Vs u u u h

i2 h@CaflaM1

i2 u @CaflaM1 u þ @CFrecup u CFrecup þ @Cprecint u Cprecint u t @CaflaM1 þ2 @CaflaM1 uða, bÞ @a @b ð15Þ

Intermediary precision The straight line fitted to three standard deviations of the spiked samples blanks with aflatoxin M1 standard solutions for 0.02, 0.05 and 0.5 mg l 1 aflatoxin M1 contaminations, measured many times during the 11 months, leads to the following models for uncertainty due to intermediary precision or intra-laboratory reproducibility: For the densitometric TLC quantification: uðprecintÞ ¼ 0:2262 CaflaM1ðmg=kgÞ

ð13Þ

The first and second forms of the above equation are based on the respective forms of the measurand equation presented in Equation (2). Here the partial derivatives (differential quotient or differential coefficient) are the sensitivity coefficients given by the following equations: @CaflaM1 ðAPDA aÞ Vr CaflaM1 ¼ CFrecup ¼ @Vp b Va Vs Vp ð16Þ

688

K.L. Carvalho et al.

@CaflaM1 Vp ðAPDA aÞ Vr CFrecup ¼ @Va b Va2 Vs CaflaM1 ¼ ð17Þ Va @CaflaM1 Vp ðAPDA aÞ ¼ CFrecup @Vr b Va Vs CaflaM1 ¼ Vr

where the partial derivatives (differential quotient or differential coefficient) are the sensitivity coefficients given by: @CaflaM1 ðLVm CResol Þ Vr ¼ CFrecup @Vp Va Vs CaflaM1 ¼ Vp

ð26Þ

ð18Þ

@CaflaM1 Vp ðAPDA aÞ Vr CFrecup ¼ @Vs b Va Vs2 CaflaM1 ¼ ð19Þ Vs @CaflaM1 Vp ðAPDA aÞ Vr CaflaM1 ¼ ¼ CFrecup b Va Vs CFrecup ð20Þ

@CaflaM1 Vp ðLVm CResol Þ Vr CFrecup ¼ @Va Va2 Vs CaflaM1 ¼ ð27Þ Va @CaflaM1 Vp ðLVm CResol Þ ¼ CFrecup @Vr Va Vs CaflaM1 ¼ Va

ð28Þ

ð21Þ

@CaflaM1 Vp ðLVm CResol Þ Vr ¼ CFrecup @Vs Va Vs2 CaflaM1 ¼ ð29Þ Vs

@CaflaM1 Vp Vr CaflaM1 ¼ CFrecup ¼ DAPDA b Va Vs APDA a ð22Þ

@CaflaM1 Vp ðLVm CResol Þ Vr CaflaM1 ¼ ¼ @CFrecup Va Vs CFrecup ð30Þ

@CaflaM1 ¼1 @Cprecint

@CaflaM1 Vp Vr CaflaM1 ¼ CFrecup ¼ @a b Va Vs APDA a ð23Þ @CaflaM1 Vp ðAPDA aÞ Vr CFrecup ¼ @b b2 Va Vs CaflaM1 ð24Þ ¼ b The only covariance taken into account was that between the intercept and the slope, u(a, b) ¼ cov(a, b), of the calibration curve of the densitometric TLC quantification obtained from WLS fitting. For visual TLC quantification, the uncertainty propagation law takes the form: uðcaflaM1Þ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi i h i uh u @CaflaM1 uðVpÞ 2 þ @CaflaM1 uðLVmÞ 2 u @Vp @LVm u u i h i u h u þ @CaflaM1 uðVrÞ 2 þ @CaflaM1 uðVaÞ 2 u @Vr @Va u ¼u h i h i2 u u þ @CaflaM1 uðVsÞ 2 þ @CaflaM1 uðC Þ u Resol @Vs @CResol u u i t h

2 h@CaflaM1

i2 þ @CaflaM1 u CF þ u C recup precint @CFrecup @Cprecint ð25Þ

@CaflaM1 ¼1 @Cprecint

ð31Þ

@CaflaM1 Vp Vr CaflaM1 ¼ CFrecup ¼ ð32Þ @LVm Va Vs LVm CResol @CaflaM1 Vp Vr CaflaM1 CFrecup ¼ ¼ @CResol Va Vs LVm CResol ð33Þ Tables 4 and 5 summarise the uncertainty combination for a blank sample spiked with aflatoxin M1 to a content of 0.05 mg l 1, which is the European Union maximum permitted value, and determined as 0.04553 mg l 1 by visual TLC quantification and as 0.05468 mg l 1 by densitometric TLC quantification. The standard uncertainties of the quantities in Tables 4 and 5, where an asterisk (*) is shown after the quantity symbol, are obtained through the uncertainty combination of the uncertainties at the lines immediately before it. For the case of the volumes, this combination is realised according to Equation (6). The graduated cylinder used to measure Vs was the unique volume measurement instrument not formally calibrated. However, the data for internal quality control of a set of such cylinders showed a maximum bias of 0.8834 ml. In a conservative way, this bias was used as the half range of the rectangular PDF (see the

0.0547

Estimated value y

0.016637

Combined standard uncertainty uc(y)

0.01237

201.082 28.9060 7.83 17.0011 1767.18 115.911 –1248.1 –1248.1 0.1094 0.1094 1 0.493827

0.25 0.0525 0.8834 0.3056

3.21

3.1824

Coverage factor k

Normal

Rectangular

B A

Normal Normal Normal Normal

Rectangular Rectangular Rectangular Normal

Rectangular Rectangular Normal Normal

Name

A A A A

B B B A

Type

Coverage Effective degrees of probability P (%) freedom eff

mg l

1 1 ml mg 1 ml mg 1 mg ml 1 mg ml 1 1

ml ml ml ml ml ml ml ml ml ml ml ml ml ml ml

ml ml ml ml ml

Units

0.052945

Expanded uncertainty U(y)

3.46410

1 1 1 1

1.73205 1.73205 1.73205 1

1.73205 1.73205 2 1

Divider, k

Probability density function (PDF) (distribution)

30.4

Relative standard uncertainty RSU

0.012370

28.90599 17.001138 115.911 –1248.1 0.018004 0.020287** 0.142556

0.288675 0.030311 0.255 0.00055 0.386364 0.072169 0.006062 0.085 0.0006 0.111671 0.144338 0.030311 0.510031 0.3056 0.612597

0.072169 0.003031 0.08 0.0003 0.107785

96.8

Relative expanded uncertainty REU

0.000283 0.000283 3.09E–05 8.75E–09 0.5 0.5 0.045529

0.000547 0.000547 0.000547 0.000547 0.000547 0.002734 0.002734 0.002734 0.002734 0.002734 0.000547 0.000547 0.000547 0.000547 0.000547

0.005468 0.005468 0.005468 0.005468 0.005468

0.01237

0.008179 0.00481 0.003586 –1.09E–05 0.009002 0.010143 0.006491

0.000158 1.66E–05 0.000139 3.01E–07 0.000211 0.000197 1.66E–05 0.000232 1.64E–06 0.0003053 7.89E–05 1.66E–05 0.000279 0.000167 0.000335

0.000395 0.000017 0.000437 0.000002 0.000589

–

1.0E þ 99

16 4 4

1.0E þ 99 1.0E þ 99 1.0E þ 99 4

1.0E þ 99 1.0E þ 99 1.0E þ 99 9

Degrees of Standard Sensitivity uncertainty u(xi) coefficient, ci ui(y) or u(y;xi) freedom i or eff

Uncertainty contribution ui(y) or u(y;xi)

Notes: Only the correlation between the intercept and the slope of the calibration line was considered according to Equation (34). **Value for the standard uncertainty of CSAA obtained without the covariance between the intercept and slope of the calibration straight line.

0.05

Quantity nominal value Y

Summary

Cprecint Sum

APDA a b Cov(a,b) CSAA* CSAA* CFrecup

0.5 0.0525 0.51 0.00055

0 0 0 0 100 0 0 0 0 20 0 0 0 0 100

CResol CVartemp CCalib CRep Vr* CResol CVartemp CCalib CRep Va* CResol CVartemp CCalib CRep Vs*

0.125 0.0105 0.17 0.0006

0.125 0.00525 0.16 0.0003

0 0 0 0 10

CResol CVartemp CCalib CRep Vp*

Interval

Value, xi

Symbol

Input or influence quantities sources of uncertainties

Table 4. Calculation of the combined uncertainty for densitometric TLC quantification of aflatoxin M1 in milk.

2.34E–08 2.387E–08

1.7E–108

2.79E–10 1.33E–10 4.13E–11 –

3.8E–116 7.5E–119 6.0E–114 1.94E–16

1.5E–114 7.5E–119 2.9E–114 8.04E–25

6.2E–115 7.5E–119 3.7E–115 9.08E–28

2.4E–113 7.5E–119 3.6E–113 8.04E–25

[ui(y)]4/ i

Food Additives and Contaminants 689

0.045

Estimated value y

0.01997

Combined standard uncertainty uc(y) 5.62

Effective degrees of freedom eff 95

Coverage probability P (%)

B A

A B

mg ml 1 mgm l 1 mg ml 1 1 mg l 1

B B B A

Type

2.5706

Coverage factor k

Rectangular Normal

Normal Rectangular

Rectangular Rectangular Rectangular Normal

Rectangular Rectangular Normal Normal

Name

0.051340

Expanded uncertainty U(y)

3.46410 1

1 1.732051

1.73205 1.73205 1.73205 1

1.73205 1.73205 2 1

Divider, k

Probability density function (PDF) (distribution)

ml ml ml ml ml

Units

Note: No possible correlations were considered.

0.05

Quantity nominal value Y

Summary

0.740740741 0.012920313

1 0

CFrecup Cprecint Sum

0.25 0.0525 0.8834 0.3056

0 0 0 0 100

CResol CVartemp CCalib CRep Vs*

0.010850779 0.035885331

0.125 0.0105 0.17 0.0006

0 0 0 0 20

CResol CVartemp CCalib CRep Va*

0 0

0.5 0.0525 0.51 0.00055

0 0 0 0 100

CResol CVartemp CCalib CRep Vr*

LVmRepet LVmResol LV

0.125 0.00525 0.16 0.0003

Interval

0 0 0 0 10

Value, xi

CResol CVartemp CCalib CRep Vp*

Symbol

Input or influence quantities sources of uncertainties

36.5263

Relative standard uncertainty RSU

0.213833 0.012920

0.010851 0.020718 0.023388

0.144338 0.030311 0.510031 0.3056 0.612597

0.072169 0.006062 0.085 0.0006 0.111671

0.288675 0.030311 0.255 0.00055 0.386364

0.072169 0.003031 0.08 0.0003 0.107785

93.8938

Relative expanded uncertainty REU

0.04553 1

0.5 0.5 0.5

0.000455 0.000455 0.000455 0.000455 0.000455

0.002276 0.002276 0.002276 0.002276 0.002276

0.000455 0.000455 0.000455 0.000455 0.000455

0.004553 0.004553 0.004553 0.004553 0.004553

Sensitivity coefficient, ci

0.009736 0.012920

0.005425 0.010359 0.011694

6.571E–05 1.380E–05 0.000232 0.000139 0.000279

0.000164 1.380E–05 0.000194 1.366E–06 0.000254

0.000131 1.380E–05 0.000116 2.504E–07 0.000176

0.000329 1.380E–05 0.000364 1.366E–06 0.000491

ui(y) or u(y;xi)

Uncertainty contribution ui(y) or u(y;xi) Standard uncertainty U(xi)

Table 5. Calculation of the combined uncertainty for visual TLC quantification of aflatoxin M1 in milk.

1.0E þ 99 1

2 1.0E þ 99

1.0E þ 99 1.0E þ 99 1.0E þ 99 4

1.0E þ 99 1.0E þ 99 1.0E þ 99 9

Degrees of freedom i or eff

9.0E–108 2.79E–08 2.83E–08

4.33E–10 1.2E–107

1.9E–116 3.6E–119 2.9E–114 9.37E–17

7.3E–115 3.6E–119 1.4E–114 3.87E–25

3.0E–115 3.6E–119 1.8E–115 4.37E–28

1.2E–113 3.6E–119 1.8E–113 3.87E–25

[ui(y)]4/ i

690 K.L. Carvalho et al.

Food Additives and Contaminants

Uncertainty contributions

0.02 Densitometric Visual

0.015 0.01 0.005 0 Comb Unc CU-prec

Int Prec

calib

Recov

Volumes

Figure 6. Contributions for the combined uncertainty of the visual and densitometric TLC quantification of aflatoxin M1 in milk for a sample blank sample spiked to a content of 0.05 mg l 1 of aflatoxin M1. For volume measurement, the bars show the combined contribution of the four measured volumes.

calibration component of Vs in Tables 4 and 5) to estimate its calibration uncertainty. The standard uncertainty of the concentration of the sample solution applied in the TLC plate, u(CSAA) ¼ 0.018004 mg ml 1, was obtained from the WLS parameters of the calibration straight line through Equation (34) (see also Equation E.3.3 in Ellison et al. 2000 and Equation 26 in Carvalho, Santos, et al. Forthcoming): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi u 2 u u ðAPDAÞ þ u2 ðaÞ þ CSAA2 u2 ðbÞ u t þ2 CSAA covða, bÞ uðCSAAÞ ¼ b2 ð34Þ As the co-variance (and correlation) between the intercept and the slope of the calibration straight line is always negative, u(CSAA) ¼ 0.020287 mg ml 1 (see the value with a double asterisk in the second line with the label CSAA in Table 4) without this correlation is higher than u(CSAA) ¼ 0.018004 mg ml 1 (see the value in the first line with the label CSAA in Table 4) when this correlation is taken into account. The combined standard uncertainties of aflatoxin M1 content as determined by the densitometric and visual TLC quantification were 0.017 and 0.020 mg l 1, respectively, for a blank sample of milk spiked with 0.05 mg l 1 (Tables 4 and 5). These combined uncertainties without the contribution of the intermediary precision are reduced to 0.011 and 0.015 mg l 1 (see the bars labelled CU-prec in Figure 6), respectively, almost equal to the uncertainties of the intermediary precisions alone: 0.0124 and 0.0129 mg l 1, respectively. Figure 6 summarises the calculations in Tables 4 and 5, showing the combined uncertainties and their contributions to the densitometric and visual TLC quantifications of a 0.05 mg l 1 aflatoxin M1 spiked blank sample milk. For comparison, the values of the combined uncertainties without the intermediary precision contribution (see CU-Prec in Figure 6) are

691

almost equal to the uncertainties of the intermediary precisions alone. This result corroborates the proposed use of the intermediary precision as a gross estimation of the analytical method’s combined uncertainty (Populaire and Gime´nez 2006). The higher contributions of the combined uncertainty come from the intermediary precision or within-laboratory reproducibility, followed by the uncertainties due to the calibration process and by the uncertainty due to the allowed recovery variation range. The two last have nearly the same contribution. The uncertainties due to the volume measurements are almost negligible and even the combination of their four contributions is very low, 0.00144 and 0.00120 for densitometric and visual quantification, respectively (see the last pair of bars in Figure 6), corresponding to less than 10% of the analytical methods’ combined uncertainties. Within these uncertainties’ sources the most important is the contribution due the volume measurement of 10 ml of the calibration standard solution, Vp, which is responsible for 41% of the total volume uncertainties in both methods.

Conclusions This paper showed in a detailed and conceptually consistent manner the estimation of the combined uncertainty of the aflatoxin M1 content in milk, determined by visual and densitometric quantification with immunoaffinity column clean-up, through the cause-and-effect methodology or bottom-up approach recommended by the ISO through the ISO GUM (ABNT, INMETRO 2003; BIPM et al. 1995). From these estimations it seems that the uncertainty of the densitometric TLC quantification is approximately between 10% and 30% lower than that of the visual TLC quantification, but their trueness is the same (INMETRO 2009; Joint Committee for Guides in Metrology (JCGM/WG 2) 2008). The main source of uncertainty in both the visual and the densitometric methods, as generally happens, was the intermediary precision, which alone is a reasonable estimation of the total (combined) analytical uncertainty (Populaire and Gime´nez 2006), which justifies the use of the top-down approach to estimate the analytical uncertainty. However, the systematic but strenuous cause-and-effect (bottom-up) approach for the estimation of analytical method uncertainty enabled the authors to understand the most important sources of uncertainties, which, if reduced, lead to an efficient analytical method precision improvement. Excepting the intermediary precision, the uncertainties due to the calibration process and the allowed recovery ratio range are equally the most important sources of uncertainties of the TLC aflatoxin M1 analysis, while the uncertainty contributions due to the volume

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measurements are practically negligible. The inclusion of the analytical method uncertainty in the calculation of the decision limit for product compliance, according European legislations, increases the importance of a realistic and conceptually consistent estimation of the combined and expanded uncertainties of the analytical procedures. By increasing the analytical precision, reducing its uncertainty, the consumer and vendor risks concerning the compliance decision are simultaneously decreased. This work shows that the use of the OLS to fit the data of measurement instrument calibration, when they are the subject of heteroskedasticity, leads to proportional bias and to incorrect estimation of the measurement uncertainty. The OLS lower and upper prediction limit lines for the heteroskedastic calibration data presented in this work are far from the fitted calibration straight line at the barycentre and MPL when compared with the WLS lower and upper prediction limit lines. This implies that the uncertainties of the analyte content at these levels, obtained from the OLS fit, is much higher than the uncertainty obtained from the WLS fit. As a consequence, the decision limit (CC ) of the OLS is also higher than the CC of the WLS. This can be a great risk for consumer health when using the decision limit as a compliance criterion.

References ABNT, INMETRO. 2003. Guia para a Expressaõ da Incerteza de Mediçaõ. Terceira ediçaõ brasileira. Rio de Janeiro (Brazil): ABNT, INMETRO. Adriaan MH, van der Veen A, Broos JM, Alink A. 1998. Relationship between performance characteristics obtained from an interlaboratory study programme and combined measurement uncertainty: a case study. Accred Qual Assur. 3:462–467. Ageˆncia Nacional de Vigilaˆncia Sanita´ria (ANVISA). 2011. Resoluçaõ RDC n 07, de 18 de fevereiro de 2011; D.O.U. – Dia´rio Oficial da Uniaõ; Poder Executivo, de 18 de março de 2011, que dispo˜e sobre os limites ma´ximos tolerados (LMT) para micotoxinas em alimentos. ANVISA, Brası´ lia, Brasil. [cited 2012 Jan 15]. Available from: http://bvsms.saude.gov.br/bvs/saudelegis/anvisa/ 2011/res0007_18_02_2011_rep.html Armishaw P. 2003. Estimating measurement uncertainty in an afternoon. A case study in the practical application of measurement uncertainty. Accred Qual Assur. 8:218–224. Barwick VJ, Ellison SLR. 1998. Estimating measurement uncertainty using a cause and effect and reconciliation approach. Part 2: Measurement uncertainty estimates compared with collaborative trial expectation. Anal Commun. 35:377–383.

Barwick VJ, Ellison SLR. 2000a. The evaluation of measurement uncertainty from method validation studies. Part 1: Description of a laboratory protocol. Accred Qual Assur. 5:47–53. Barwick VJ, Ellison, SLR. 2000b. Protocol for uncertainty evaluation from validation data. January, LGC/VAM/ 1998/088. Valid Analytical Measurement – VAM, Department of Trade and Industry, Laboratory of Government Chemists – LGC, Tedington, UK. Barwick VJ, Ellison SLR, Rafferty MJQ, Gill RS. 2000. The evaluation of measurement uncertainty from method validation studies. Part 2: The practical application of a laboratory protocol. Accred Qual Assur. 5:104–113. BIPM, IEC, IFCC, ISO, IUPAC, IUPAP, OIML. 1995. Guide to the expression of uncertainty in measurement. Geneva (Switzerland): International Organization for Standardization (ISO). Byrne D. 2000. Written question E2343/99 by Robert Evans (PSE) to the Commission. Aflatoxin fungus. Official Journal of the European Union C225 E, 08/08/2000, vol. 43, pp. 0109-0110. [cited 2012 Jan 15]. Available from: http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri¼OJ:C:2000:225E:0109:0110:EN:PDF. Calaresu G, Piras P, Carta G, Galarini R, Chessa G. 2006. Estimation of measurement uncertainty for the determination of aflatoxin M1 in milk using immunoaffinity clean-up procedures. Accred Qual Assur. 11:10–16. Carvalho KL, Gonc¸alves GAA, Neres CS, Almeida NG, Almeida MI, Santos EA, Vargas EA, Magalha˜es WF. Forthcoming. Modeling uncertainty estimation for mycotoxins analysis at the MAPA/LANAGRO-MG/LACQSA, within the ISO-GUM framework – Part III: Determination of aflatoxin BG using immunoaffinity column purification step and quantification by high performance liquid chromatography (HPLC). Food Addit Contam. To submit. Carvalho KL, Santos EA, Vargas EA, Magalha˜es WF. Forthcoming. Modeling uncertainty estimation for mycotoxins analysis at the MAPA/LANAGRO-MG/LACQSA, within the ISO-GUM framework – Part I: General approach. J Braz Chem Soc. To submit. Commission of the European Communities. 1998. Commission Directive 98/53/EC, laying down the sampling methods and the methods of analysis for the official control of the levels for certain contaminants in foodstuffs. Off J Eur Comm. L201:93–101. Commission of the European Communities. 2001. Commission Regulation (EC) No. 466/2001, setting maximum levels for certain contaminants in foodstuffs. Off J Eur Comm. L77:1–13. Commission of the European Communities. 2002. Commission Decision 2002/657/EC, Implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results. Off J Eur Comm. L221:8–36. Commission of the European Communities. 2004. Commission Directive 2004/43/EC, amending Directive 98/53/EC and Directive 2002/26/EC as regards sampling methods and methods of analysis for the official control of

Food Additives and Contaminants the levels of aflatoxin and ochratoxin A in food for infants and young children. Off J Eur Comm. L113:14–16. Commission of the European Communities. 2006. European Regulation (EC) No. 401/2006 of 23 February 2006. Laying down the methods of sampling and analysis for the official control of the levels of mycotoxins in foodstuffs. Off J Eur Union. L70:12–34. Dragacci S, Grosso F, Gilbert J. 2001. Immunoaffinty column clean-up with liquid chromatography for determination of aflatoxin M1 in liquid milk: collaborative study. J AOAC Int. 84(2):437–443. Ellison SLR. 1998. ISO uncertainty and collaborative trial data. Accred Qual Assur. 3:95–100. Ellison SLR, Barwick VJ. 1998a. Estimating measurement uncertainty reconciliation using a cause and effect approach. Accred Qual Assur. 3:101–105. Ellison SLR, Barwick VJ. 1998b. Using validation data for ISO measurement uncertainty estimation, Part 1. Principles of an approach using cause and effect analysis. Analyst. 123:1387–1392. Ellison SLR, Rosslein M, Williams A. 2000. EURACHEM/ CITAC guide quantifying uncertainty in analytical chemistry. 2nd ed. EURACHEM;CITA. [cited 2012 Jan 15]. Available from: http://www.eurachem.org/guides/pdf/ QUAM2000-1.pdf. European Commission. 2010. Commission Regulation (EU) No. 165/2010 of 26 February 2010, amending Regulation (EC) No. 1881/2006 setting maximum levels for certain contaminants in foodstuffs as regards aflatoxins. Off J Eur Comm. L50:8–12. Instituto Nacional de Metrologia Normalizaçaõ e Qualidade Industrial (INMETRO). 2009. Vocabula´rio Internacional de Metrologia – Conceitos fundamentais e gerais e termos associados (VIM 2008). Primeira Ediçaõ Brasileira do VIM (Traduçaõ autorizada pelo BIPM da terceira ediçaõ internacional do VIM – International vocabulary of metrology – basic and general concepts and associated terms – JCGM 200:2008); [cited 2010 Apr 21]. Available

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from: http://www.inmetro.gov.br/infotec/publicacoes/ VIM_2310.pdf/ International Organization for Standardization (ISO). 1997. ISO 11843-1:1997(E/F), Capability of detection, Part 1: Terms and definitions. Geneva (Switzerland): ISO. International Organization for Standardization (ISO). 2000. ISO 11843-2:2000(E), Capability of detection, Part 2: Methodology in the linear calibration case. Geneva (Switzerland): ISO. International Organization for Standardization (ISO). 2005. ISO/IEC 17025:2005, General Requirements for the competence of calibration and testing laboratories. Geneva (Switzerland): ISO. Joint Committee for Guides in Metrology (JCGM/WG 2), BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP and OIML, JCGM 200:2008 (E/F). 2008. International vocabulary of metrology – basic and general concepts and associated terms (VIM). Vocabulaire international de me´trologie – Concepts fondamentaux et geńe´raux et termes associe´s (VIM). Document produced by Working Group 2 of the Joint Committee for Guides in Metrology (JCGM/WG 2). [cited 2012 Jan 15]. Available from: http:// www.bipm.org/en/publications/guides/vim.html/ MERCOSUR/GMC/RES. 2002. N 25/02 Regulamento Tećnico Mercosul Sobre Limites Ma´ximos de Aflatoxinas Admissı´ veis no Leite, Amendoim e Milho (revogaçaõ da RES. GMC N 56/94). XLVI GMC – Buenos Aires, 20/VI/02. Populaire S, Gimeńez EC. 2006. A simplified approach to the estimation of analytical measurement uncertainty. Accred Qual Assur. 10:485–493. World Health Organization (WHO) and International Agency for Research on Cancer (IARC). 2002. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Vol. 82: Some traditional herbal medicines, some mycotoxins, naphthalene and styrene summary of data reported and evaluation, Last updated: 4 December 2002. Available from: http://monographs. iarc.fr/ENG/Monographs/vol82/volume82.pdf/

Food Additives and Contaminants Vol. 29, No. 4, April 2012, 694–703

Co-occurrence of aflatoxins B1, B2, G1 and G2, ochratoxin A, zearalenone, deoxynivalenol, and citreoviridin in rice in Brazil M.I. Almeida*, N.G. Almeida, K.L. Carvalho, G.A.A. Gonc¸alves, C.N. Silva, E.A. Santos, J.C. Garcia and E.A. Vargas Laboratory of Quality Control and Safety Food – LACQSA/MAPA, Av. Raja Gabaglia, 245, Cidade Jardim, CEP 30380-090 – Belo Horizonte, MG, Brazil-Lacqsa (Received 22 November 2010; final version received 16 December 2011) A total of 230 samples of processed rice and its sub-products or derived products were analysed to establish the co-occurrence of several mycotoxins. Samples were analysed in the period 2007–2009 due to the outbreak of beriberi associated with the consumption of rice stored in inappropriate conditions in Brazil. According to data from the Ministry of Health, 323 cases of disease were registered in 2006, of which at least 47 cases resulted in death. The occurrence of total aflatoxin (AFT) (aflatoxin B1 þ B2 þ G1 þ G2), ochratoxin A (OTA), zearalenone (ZON), deoxynivalenol (DON), and citreoviridin (CTV) was 58.7%, 40.0%, 45.2%, 8.3% and 22.5%, respectively. From 166 rice samples analysed, 55% had levels <0.11 mg kg 1 for AFT. For OTA and ZON, of 165 rice samples analysed, 28% and 29% were contaminated with levels from 0.20 to 0.24 mg kg 1 and from 3.6 to 290.0 mg kg 1, respectively. One sample (0.6%) was contaminated with 4872.0 mg kg 1 of ZON. A total of 91% of rice samples (n ¼ 165) did not contain detectable DON (<30.00 mg kg 1), although the highest level of contamination was found to be 244 mg kg 1. From the total of 65 samples analysed, 94% had no detectable CTV (<0.9 mg kg 1), with a range from 0.9 to 31.1 mg kg 1 in 6% of the samples. The highest levels of contamination were found in rice sub-products or derived products from the husk and rice bran. Co-occurrence was observed for AFT and ZON in 17.0%, AFT and OTA in 24.2%, AFT and CTV in 6.2%, OTA and CTV in 4.6%, and ZON and CTV in 3.1%. These fractions were also the major contributors for the co-occurrence. The results found show the necessity of monitoring rice production. Keywords: chromatography – HPLC; mycotoxins; rice

Introduction Rice (Oryza sativa L.) is the predominant staple food for 17 countries in Asia and the Pacific, nine countries in North and South America, and eight countries in Africa (Food and Agriculture Organization of the United Nations (FAO) 2004). In Latin America and the Caribbean, Brazil is the largest rice producer (FAO 2004). The Brazilian paddy rice production for the 2009/2010 crop was estimated at 12 million tones, as is shown in Table 1 (Companhia Brasileira do Abastecimento (CONAB) 2010) and represented 1.9% of the world’s paddy rice production (Joint FAO/WHO Expert Committee on Food Additives (JECFA) 2008). In 2006, Brazil’s annual per capita rice consumption was estimated at about 47 kg for milled and 70 kg for paddy rice (Wander et al. 2007). According to the FAO, Brazil’s per capita rice consumption in 2007 was 33.24 and 49.83 kg for milled and paddy rice, respectively (FAOSTAT 2010). In the developing world, where climatic and

*Corresponding author. Email: maria.almeida@agricultura.gov.br ISSN 1944–0049 print/ISSN 1944–0057 online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/19440049.2011.651750 http://www.tandfonline.com

crop storage conditions are frequently conducive to fungal growth and mycotoxin production, much of the population relies on subsistence farming or on unregulated local markets (Shephard 2008). In this regard, due to inappropriate storage conditions rice can be an ideal substrate for mycotoxin-producing fungi (Reiter et al. 2010) and thus mycotoxin contamination may represent a major public health problem considering the high consumption levels of rice by the Brazilian population. Mycotoxins are secondary metabolites of moulds that have adverse effects on humans, animals, and crops that result in illnesses and economic losses (Zain 2011). The five most important mycotoxins that occur naturally in agricultural products are the aflatoxins: ochratoxin (OTA), deoxynivalenol (DON), zearalenone (ZON); and the fumonisins (Miller 1995). Aflatoxins are highly toxic compounds; and aflatoxin B1 is one of the most potent hepatocarcinogens known (JECFA 2008). The most important aflatoxin

Food Additives and Contaminants Table 1. Rice production in Brazil.

Region/UF North Roraima Rondoˆnia Acre Amazonas Amapa´ Para´ Tocantins Northeast Maranha˜o Piauı´ Ceara´ Rio Grande do Norte Paraı´ ba Pernambuco Alagoas Sergipe Bahia Central West Mato Grosso Mato Grosso do Sul Goia´s Distrito Federal Southeast Minas Gerais Espı´ rito Santo Rio de Janeiro Sa˜o Paulo South Parana´ Santa Catarina Rio Grande do Sul North/Northeast Central–South Brazil

Crop estimates, 2009/10 (thousand tonnes)

Percentage against Brazilian production

946.2 82.5 146.4 19.1 10.0 3.9 298.2 386.1 1145.8 679.6 218.6 100.5 5.6 9.7 26.0 14.5 34.2 57.1 1157.1 780.3 170.0 206.8 – 195.7 118.8 4.4 7.4 65.1 8571.5 164.4 1063.8 7343.3 2092.0 9924.3 12 016.3

7.9 0.7 1.2 0.2 0.1 0.0 2.5 3.2 9.5 5.7 1.8 0.8 0.0 0.1 0.2 0.1 0.3 0.5 9.6 6.5 1.4 1.7 – 1.6 1.0 0.0 0.1 0.5 71.3 1.4 8.9 61.1 17.4 82.6 100.0

Source: Companhia Brasileira do Abastecimento (CONAB) (2010).

producers from a public health point of view are members of section Flavi, in particular Aspergillus flavus and A. parasiticus (Pildain et al. 2008). Due to its origin, rice could be contaminated with aflatoxins, which are primarily produced by A. flavus and A. parasiticus as well as phenotypically similar species of A. flavus such as A. nomius (Kurtzman et al. 1987; Frisvad et al. 2007; Olsen et al. 2008). Aflatoxins have also been shown to be produced by A. pseudotamarii (Ito et al. 2001), A. bombycis (Peterson et al. 2001), A. toxicarius (Murakami 1971; Murakami et al. 1982; Frisvad et al. 2004), A. parvisclerotigenus (Saito and Tsurota 1993; Frisvad et al. 2004), A. arachidicola sp. nov. (Pildain et al. 2008), A. minisclerotigenes (Pildain et al. 2008), A. ochraceoroseus (Frisvad et al. 1999;

695

Klich et al. 2000), A. rambelii (Frisvad et al. 2005), Emericella astellata (Frisvad et al. 2004), and E. venezuelensis (Frisvad and Samson 2004). OTA is produced by a single Penicillium species, P. verrucosum, by A. ochraceus and several related Aspergillus species and by A. carbonarius, with a small percentage of isolates of the closely related A. niger (JECFA 2001). The latest information indicates that A. ochraceus is an uncommon fungus, and isolates do not often produce OTA. However, two recently described species, A. westerdijkiae and A. steynii, split off from A. ochraceus, are the major producers. (JECFA 2008). Although the widespread occurrence of ochratoxigenic species has been confirmed, each shows different behaviours with respect to ecological niches, the products (substrates) affected and their geographical occurrence. Thus the origin of a certain cereal crop is important since the mycotoxin-producing fungal species differ in their ecological niches (Duarte et al. 2010). The kidney is the major target organ for the adverse effects of OTA; and short-term toxicity studies in mice, rats, dogs and pigs have shown dose- and timedependent development of progressive nephropathy (JECFA 2008). DON or vomitoxin belongs to the class of mycotoxins called trichothecenes of the Type B group; and it is non-fluorescent (Richard 2007). Surveys have shown that DON occurs predominantly in grains such as wheat, barley, oats, rye and maize and less often in rice, sorghum and triticale. The occurrence of DON is associated primarily with Fusarium graminearum (Gibberella zeae) and F. culmorum, both of which are important plant pathogens, causing Fusarium head blight in wheat and Gibberella ear rot in maize (JECFA 2001). DON may co-exist with zearalenone, another mycotoxin produced by these organisms (Richard 2007). Although DON is one of the least acutely toxic trichothecenes, it should be treated as an important food safety issue because it is a very common contaminant of grain (Rotter et al. 1996). When ingested in high doses by agricultural animals, DON causes nausea, vomiting, and diarrhoea (Bennett and Klich 2003). Many outbreaks of acute disease involving nausea, vomiting, gastrointestinal upset, dizziness, diarrhoea and headache have been reported in Asia, which have been attributed to the consumption of grains contaminated with Fusarium spp. and, more recently, to the presence of DON at concentrations of 3–93 mg kg 1 in grains for human consumption (JECFA 2008). ZON is a non-steroidal oestrogenic mycotoxin biosynthesised through a polyketide pathway by a variety of Fusarium fungi, including F. graminearum (G. zeae), F. culmorum, F. cerealis, F. equiseti, F. crookwellense and F. semitectum, which are common soil fungi, in temperate and warm countries, and are regular contaminants of cereal crops

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M.I. Almeida et al. Table 2. Quality control samples performance for the analytical methods used.

Mycotoxin

Range of contamination in the spiked sample (mg kg 1)

AF B1 AF B2 AF G1 AF G2 OTA ZON DON CTV

2.03–4.04 0.41–1.52 1.99–4.12 0.40–1.50 4.51–7.21 116.6–741.0 227.50–413.98 19.2–37.7

Number of spiked samples analysed 36

34 25 37 13

worldwide (Bennett and Klich 2003). The most notable effect of ZON is that it causes precocious development of mammae and other oestrogenic effects in young gilts as well as prepucial enlargement in young barrows (Richard 2007). A provisional maximum tolerable daily intake (PMTDI) for ZON of 0.5 mg kg 1 of body weight was established by JECFA (Zinedine et al. 2007). Citreoviridin (CTV) is a toxic secondary metabolite originally isolated from the metabolism of P. citreonigrum, also produced by P. ochrasalmoneum and P. pulvillorum strains (Ueno 1985). It occurs naturally in rice and corn and is considered as a neurotoxic mycotoxin (Nishie et al. 1988; Stubblefield et al. 1988) and a potent inhibitor of mitochondrial ATPase (Linnett et al. 1978; Hongsuk et al. 1996). CTV interferes with the metabolism of nerve and muscle tissues, competing with the absorption of thiamin (vitamin B1) by the cells of these tissues, thus causing deficiency of vitamin B1, known as beriberi (Ministry of Health 2007). The occurrence of beriberi in Japan and Asian countries is attributed to the consumption of mouldy and yellow rice (Wicklow et al. 1988). Considering the problems related to an outbreak of beriberi in the state of Maranha˜o, the objective was to evaluate the occurrence of mycotoxins ZON, aflatoxin B1, B2, G1 and G2, OTA, DON and CTV in samples of rice, and its processing fractions (broken, rice bran and rice husk).

Materials and methods Quality control The laboratory was accredited to ISO 17025:2005 and used methods validated by collaborative studies or was in-house validated. Lacqsa has regularly participated in proficiency testing, in particular FAPASÕ (Food and Environment Research Agency), achieving results with satisfactory z-scores (smaller than j2j). For ZON in wheat, maize and animal feed z-scores ranged

Average recovery (%)

Maximum standard deviation

Maximum CV (%)

102 97 88 90 95 94 93 95

0.63 0.30 0.62 0.28 0.67 20.31 50.98 2.70

19.89 22.00 23.36 21.82 11.47 12.02 17.66 15.40

from 0.1 to 1.9; and for DON in wheat and maize z-scores were 1.4 and 1.10, respectively; aflatoxin BG in peanuts, maize, brazil nut, rice and food z-scores ranged from 1.7 to 1.5; and for OTA in instant coffee and roasted coffee z-scores were from 0.5 to 1.44. To ensure the quality and reliability of the results, all samples were analysed together with a quality control sample and spiked with standard solutions at levels shown in Table 2. The analytical methods used by Lacqsa meet the performance criteria as determined by the European Commission (EC) (2006) for AFT, OTA, ZON and DON. For CTV, the recovery was determined by the laboratory as 65–110%.

Samples A total of 230 samples of rice and its processing fractions (bran, rice husk and broken) were collected from different regions of the country in the period of 2007–2009. These samples were ground using screwtype mills ‘Arbel’, with a maximum particle size of 1 mm, homogenised and stored at 15 C.

Standard solutions Standard solutions of AFB1, AFB2, AFG1, AFG2, OTA, and ZON were prepared and standardised by spectrophotometer in accordance with AOAC International (2005); the standard solution of CTV was prepared according to Ueno (1970), as shown in Table 3. The DON standard was weighed on a calibrated analytical balance (accuracy to 0.0001 g); the standard solutions were prepared by dissolving the solid standard in ethyl acetate (Shepherd and Gilbert 1988).

Determination of aflatoxins Sodium chloride (5 g) was added to 50 g of rice samples or their processing fractions (husk, bran and broken)

Food Additives and Contaminants

697

Table 3. Mycotoxin solution standardisation parameters.

Mycotoxin AF B1 AF B2 AF G1 AF G2 OTA ZON CTV

Wavelength range (nm)

Molecular weight (g mol 1)

Range of concentration (mg ml 1)

Solvent used

330–370 330–370 330–370 330–370 300–400 280–380 200–400

312 314 328 330 403 318 402

8–10 8–10 8–10 8–10 40 50 5

Toluene:acetonitrile (9:1, v/v) Toluene:acetonitrile (9:1, v/v) Toluene:acetonitrile (9:1, v/v) Toluene:acetonitrile (9:1, v/v) Toluene:acetic acid (99:1, v/v) Benzene Methanol

at room temperature and were extracted with and 300 ml of methanol:water (80:20, v/v) for 3 min using a sample homogeniser (Omni MixerTM, GA, USA) at about 800 rpm. The extract was filtered through folded qualitative filter paper, and then through a vacuum glass fibre membrane Whatman GF/BTM (Maidstone, England) 1 mm. An aliquot of 10 ml of the filtered extract was diluted with 60 ml of PBS 0.1%, and the entire volume of this solution (70 ml) was purified by immunoaffinity column (R-biopharmTM , Darmstadt, Germany) at a flow rate of 2–3 ml min 1; the column was washed with 15 ml of water at the same flow and then dried, and the toxin eluted with 2 ml of methanol, allowing methanol in contact with the antibodies for 2 min, and then a positive pressure for elution of the toxin in a stream was done for 2–3 ml min 1. The eluate was evaporated under nitrogen or compressed air in the heating bath and stirring at 40 C. After evaporation the residue was taken up with 3000 ml of solution of methanol:water (2:3, v/v) and homogenised. The steps for separation, detection and quantification were performed by HPLC using column C18 250 4.6 mm and 5 mm particles (ShimadzuTM, Kyoto, Japan), a fluorescence detector with excitation at 360 nm and emission at 420 nm, a mobile phase of water:acetonitrile:methanol (6:2:2, v/v/v) with the addition of 340 mg of potassium bromide and 700 ml of 4 M nitric acid for each 2000 ml of mobile phase, a flow of 1.0 ml min 1, and an injection volume 50 ml, with post-column derivatisation using a Kobra CellTM (R-biopharm, Darmstadt, Germany) electrochemical cell (AOAC International 2005). Method performance using control samples The limits of detection for AFB1, AFB2, AFG1 and AFG2 were 0.03, 0.01, 0.01 and 0.01 mg kg 1, respectively. The average recoveries of spiked samples were 102%, 97%, 88% and 90% for AFB1, AFB2, AFG1 and AFG2, respectively; and contamination levels from 2.03 to 4.04 mg kg 1 for AFB1, from 0.41 to 1.52 mg kg 1 for AFB2, from 1.99 to 4.12 mg kg 1 for AFG1, and from 0.40 to 1.50 mg kg 1 for AFG2 (Table 2).

Molar absorptivity (") 19,300 21,000 16,400 18,300 5440 6060 44,925

(350 nm) (350 nm) (350 nm) (350 nm) (333 nm) (317 nm) (383 nm)

Determination of OTA For the analysis of OTA, samples (25 g) were extracted with 200 ml of 3% sodium bicarbonate:methanol (1:1, v/v) and homogenised for 5 min at 800 rpm (Omni mixerTM). The extract was filtered through folded qualitative filter paper, and then through a vacuum glass fibre membrane Whatman GF/BTM 1 mm. Dilution was made with 4 ml of filtered extract to 100 ml with PBS buffer solution 0.1%. The diluted extract was purified by immunoaffinity columns at a flow rate of 2–3 ml min 1, the column was washed with 10 ml of water (2–3 ml min 1) and after drying the toxin was eluted with 4 ml methanol. After 3 min there was positive pressure to control the flow (2–3 ml min 1). Follow up consisted of evaporation of the eluate in a heating bath and stirring at 40 C under nitrogen or compressed air. The residue was dissolved by adding 300 ml of methanol:acetonitrile:water:acetic acid (35:35:29:1, v/v/v/v), followed by homogenisation. Separation, quantification and detection were performed by HPLC using column C18 250 4.6 mm and 5 mm particles (ShimadzuTM), a fluorescence detector with excitation at 332 nm and emission at 476 nm, a mobile phase of methanol:acetonitrile:water:acetic acid (35:35:29:1, v/v/v/v) at a flow rate of 0.8 ml min 1 and an injection volume of 20 ml (Vargas et al. 2005; AOAC International 2005). Method performance using control samples The limit of detection was 0.10 mg kg 1; the average recovery of samples spiked with OTA was 95% (range 4.51–7.21 mg kg 1), according to Table 2.

Determination of ZON ZON was determined by extraction of the sample (25 g) with 100 ml of acetonitrile:water (84:16, v/v) for 5 min in a sample homogeniser (Omni mixerTM) at high speed. Approximately 3 g of Celite were added to the vial and homogenised manually; after decanting the solution the extract was filtered through qualitative

698

M.I. Almeida et al. Table 4. Occurrence of AFB1, AFT OTA, ZON, DON and CTV in rice samples and their fractions: mycotoxin contamination in the samples (mg kg 1). Matrix AFB1 Rice Rice husk Bran Broken AFT Rice Rice husk Bran Broken OTA Rice Rice husk Bran Broken ZON Rice Rice husk Bran Broken DON Rice Rice husk Bran Broken CTV Rice Rice husk Bran Broken

Minimum

Average

Maximum

Median

95th percentile

n.d. n.d. n.d. n.d.

9.09 6.09 38.65 5.60

158.14 23.34 180.74 1707

2.49 3.79 18.27 4.20

35.04 17.82 104.56 15.82

n.d. n.d. n.d. n.d.

9.37 7.55 44.65 6.72

176.31 31.72 207.04 19.42

1.86 5.02 20.11 5.13

40.66 24.11 118.95 19.36

n.d. n.d. n.d. n.d.

1.78 3.91 11.63 1.02

30.24 51.48 43.02 1.79

0.64 1.36 4.84 0.97

3.95 3.85 30.70 1.74

n.d. 28.2 13.9 n.d.

143.0 1781.6 579.3 17.2

4872.5 15650.6 2676.1 31.4

26.4 574.3 405.5 18.1

199.1 7402.7 1546.0 28.9

n.d. n.d. n.d. n.d.

119.33 56.00 300.00 n.d.

244.00 56.00 300.00 n.d.

116.00 56.00 300.00 n.d.

227.20 56.00 300.00 n.d.

n.d. n.d. n.d. n.d.

8.8 10.4 12.2 4.0

31.1 38.7 51.0 11.2

1.6 4.7 4.6 1.6

26.7 34.4 39.6 10.3

filter paper and then a vacuum membrane fibreglass Whatman GF/BTM 1 mm. An aliquot of 5 ml of filtered extract was purified through a column Romer MycoSep 224TM (MO, USA), and 2.5 ml of purified extract were collected. This extract was evaporated under nitrogen or compressed air in the heating bath with stirring, and then resumed with 300 ml of a solution of methanol:water (70:30, v/v), followed by homogenisation. It was injected in an HPLC apparatus for separation, quantification and detection, with the following conditions: column C18 250 4.6 mm and 5 mm particles (ShimadzuTM), a fluorescence detector with excitation at 280 nm and emission at 465 nm, a mobile phase of methanol:water (70:30, v/v), a flow rate 0.8 ml min 1 and injection volume of 20 ml (Silva and Vargas 2001).

Method performance using control samples The average recovery for ZON in spiked samples was 94%; the range of contamination was from 116.6 to

741.0 mg kg 1 (Table 2). The limit of detection was 3.6 mg kg 1.

Determination of DON DON was determined in samples by extraction of sample (20 g) by adding 80 ml of water and 4 g of polyethylene glycol 6000, and agitation of the samples in a homogeniser (Omni mixerTM) for 3 min at high speed. The extract was centrifuged for 20 min at 2700 rpm and then filtered using a membrane filter (0.45 mm), followed by the purification of 1 ml of filtered extract using immunoaffinity columns (DONTest; VicamTM, MA, USA) at a flow rate of 1 drop per second. The column was then washed with 5 ml of water in the same stream, and after drying was eluted with 2 ml of acetonitrile in the same stream. The eluate was evaporated in the heating bath and stirred at 40 C in a nitrogen atmosphere or compressed air, and was resumed with 500 ml of solution of methanol:water (9.5:90.5, v/v) and homogenised.

166 a

18b

Matrix

Rice

Rice husk

Bran

Broken

n.d.c 0.06 10.00 >10.00 20.00 >20.00 30.00 >30.00 n.d.c 0.06 10.00 >10.00 20.00 >20.00 30.00 >30.00 n.d.c 0.06 10.00 >10.00 20.00 >20.00 30.00 >30.00 n.d.c 0.06 10.00 >10.00 20.00 >20.00 30.00 >30.00

Range (mg kg 1) 60 31 4 2 3 4 74 19 4 0 11 26 21 0 42 6 72 22 0 0

7 3 5 1 20 5 1 0 2 5 4 0 8 1 13 4 0 0

99 52

Number of samples n.d.d 0.11 10.00 >10.00 20.00 >20.00 30.00 >30.00 n.d.d 0.11 10.00 >10.00 20.00 >20.00 30.00 >30.00 n.d.d 0.11 10.00 >10.00 20.00 >20.00 30.00 >30.00 n.d.d 0.11 10.00 >10.00 20.00 >20.00 30.00 >30.00

Range (mg kg 1)

8 1 12

1 2 5

5 1 20

91 56

Number of samples

AF total

42 6 67

4 11 26

3 4 74

55 34

n.d.e 0.20 10.00 >10.00

Range (mg kg 1)

10 8

5 69

5 21

118 45

Number of samples

OTA

56 44

26 363

19 78

72 27

n.d.f 3.6 200.0 >200.0 400.0 >400.0

Range (mg kg 1)

Notes: A total of 165 samples were analysed for the determination of OTA, ZON, DON and CTV. b Seventeen samples were analysed for the determination of DON and CTV. c n.d. <0.06 mg kg 1. d n.d. <0.11 mg kg 1. e n.d. <0.20 mg kg 1. f n.d. <3.6 mg kg 1. g n.d. <30.0 mg kg 1. h n.d. <0.9 mg kg 1.

Total samples analysed

AFB1

10 8

0 4

0 7

116 46

Number of samples

ZON

56 44

0 21

119

0 26

70 28

n.d.g 30.00

Range (mg kg 1)

Table 5. Distribution of samples analysed per matrix and range of contamination by AFB1, AF total, OTA, ZON, DON and CTV.

17 0

18 1

26 1

150 15

Number of samples

DON

100 0

95 5

96 4

91 9

n.d.h 0.9 10.0 >10.0 30.0 >30.0

Range (mg kg 1)

10 6

10 5

17 7

61 3

Number of samples

CTV

59 35

53 26

63 26

94 5

Food Additives and Contaminants 699

700

M.I. Almeida et al.

Table 6. Co-occurrence of mycotoxins in the polished rice samples and their fraction.

Matrix Rice Rice husk Bran Broken Rice Rice husk Bran Broken Rice Rice husk Bran Broken Rice Rice husk Bran Broken Rice Rice husk Bran Broken

Toxins AF total þ OTA

AF total þ ZEA

ZEA þ CTV

AF total þ CTV

OTA þ CTV

Number of samples

40 22 14 8 28 26 17 8 2 10 7 4 4 9 7 7 3 7 6 4

24 82 74 44 17 96 90 44 3 37 37 24 6 33 37 41 5 26 32 24

Quantification, separation and detection were performed by HPLC using a UV-visible detector at a wavelength of 220 nm, C18 column, 150 4.6 mm, 5 mm particles (ShimadzuTM), at a temperature of 28 C, at a flow rate of 1 ml min 1, a mobile phase of methanol:water (15:85, v/v) and injection of 30 ml (Neumann et al. 2006). Method performance using control samples The average recovery was 93% at concentrations from 227.50 to 413.98 mg kg 1 (Table 2). The limit of detection was 30.00 mg kg 1.

Determination of citreoviridin A total of 129 samples were analysed for CTV. The determination of CTV was performed by extracting 50 g samples of milled rice and broken rice, or 25 g of rice husk and bran of rice with 150 ml of dichloromethane and stirring to homogenise the samples (Omni mixerTM) for 3 min at 800 rpm, followed by vacuum filtration through a membrane of glass fibre Whatman GF/BTM 1 mm. An aliquot of 20 ml of filtered extract was purified using cartridge solidphase extraction C18 500 mg/3 ml WatersTM (MA, USA). The cartridge was conditioned with 5 ml of dichloromethane in a stream at 7 ml min 1; the extract was then transferred to the cartridge for purification in the same flow, the cartridge then washed with 5 ml of dichloromethane, and the purified extract collected

in a glass test tube. After evaporation in the heating bath and stirring at 40 C in an atmosphere of nitrogen or compressed air, the extracts were taken up with 400 ml of methanol : hexane (2:1, v/v) followed by homogenisation. The steps for separation, detection and quantification were performed by HPLC using a C18 column 250 4.6 mm and 5 mm particles (ShimadzuTM), a UV-visible detector with a wavelength of 385 nm, a mobile phase of methanol:formic acid 1% (65:35, v/v), at a flow rate of 0.8 ml min 1 and injection volume of 50 ml. Method performance using control samples The average recovery of the method was 95% (range ¼ 19.2–37.7 mg kg 1), according to Table 2. The limit of detection was 0.9 mg kg 1.

Results and discussion The occurrences of total aflatoxin (AFT) (aflatoxin B1 þ B2 þ G1 þ G2), OTA, ZON, DON, and CTV were 58.7%, 40.0%, 45.2%, 8.3% and 22.5%, respectively (Table 5). Of 230 samples, 135 were contaminated with AFT in the range 0.11–207.04 mg kg 1 with a mean contamination of 13.13 mg kg 1. Among these samples AFB1 was the toxin most commonly found (55.7%) at levels from 0.08 to 180.74 mg kg 1, followed by AFB2 (53.9%) with contamination from 0.02 to 17.19 mg kg 1, AFG1 (37.0%) at levels from 0.02 to 12.03 mg kg 1, and AFG2 (23.5%) with contamination from 0.01 to 0.69 mg kg 1 (Table 5). From 166 rice samples analysed, 55% had non-detectable levels (<0.11 mg kg 1) of AFT. The highest percentage of rice samples analysed (60%) had non-detectable levels (<0.06 mg kg 1) for AFB1 (Table 5), followed by 31% of samples showing contamination levels from 0.06 to 10.00 mg kg 1, the remaining contamination being >10.00 to 20.00 mg kg 1 (4%), >20.00 to 30.00 mg kg 1 (2%), and >30.00 mg kg 1 (3%). Only 4% of the samples of the rice husk fraction showed non-detectable results (<0.06 mg kg 1), 74% of samples had contamination between 0.06 and 10.00 mg kg 1, 19% from >0.00 to 20.00 mg kg 1, and 4% of samples from >20.00 to 30.00 mg kg 1. Samples of rice husk bran showed the following distribution: 11% not detectable (<0.06 mg kg 1), 26% from >0.06 to 10.00 mg kg 1, 21% from >20 to 10.00 mg kg 1, and 42% contained >30.00 mg kg 1. In the broken fraction 6% of the samples had non-detectable levels (<0.06 mg kg 1), 72% between 0.06 and 10.00 mg kg 1, and 21% from >10.00 to 20.00 mg kg 1. The range distribution of contamination by AF total is similar to that of AFB1 (Table 5). For OTA and ZON, 165 rice samples were analysed and 28% and 29% were contaminated from 0.20 to

0.1 0.5 7.0

0.014 0.070 1.000

PTDI (mg kg 1 bw)c

Notes: aAdapted from http://jecfa.ilsi.org/serch.cfm/. b Provisional tolerable weekly intake. c Provisional tolerable daily intake.

OTA ZON DON

Mycotoxin

PTWI (mg kg 1 bw)b 0.86 4.29 60.00

Daily human safe intake (mg day 1), 60 kg bw 30.2 4872.5 244.0

Maximum contamination (mg kg 1) 3.0 487.3 24.4

Daily intake (mg), considering 100 g of rice 352.8 11 369.2 40.7

Percentage relative to the PTDI, 60 kg bw

0.50 42.47 119.33

Mean contamination (mg kg 1)

0.05 4.20 11.90

Daily intake (mg), considering 100 g of rice

5.91 99.10 19.90

Percentage relative to the PTDI

Table 7. Contribution of rice contamination found in relation to the provisional tolerable daily intake (PTDI) for OTA, DON and ZON: JECFAâ&#x20AC;&#x2122;s evaluation of tolerable intake.a

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702

M.I. Almeida et al.

0.24 mg kg 1 and from 3.6 to 290.0 mg kg 1, respectively. One sample (0.6%) was contaminated with 4872.0 mg kg 1 of ZON. The highest levels of contamination by OTA, ZON and CTV (Table 4) were found in the fractions bran and husk fractions. The samples for the determination of DON were mostly not detectable (<30.00 mg kg 1) in 91% of rice samples, in 96% of rice bran, in 95% bran of rice husk and in 100% samples of broken (Table 5). The highest DON contamination was 244.00 mg kg 1 for rice, 300 mg kg 1 for bran, and 56 mg kg 1 for husk (Table 4). From the total of 65 rice samples analysed, 94% had non-detectable levels (<0.9 mg kg 1) for CTV, and 6% of samples contained CTV in a range from 0.9 to 31.1 mg kg 1. The co-occurrence of mycotoxins in the samples is shown in Table 6, with the highest co-occurrence observed for AFT and OTA in which there was contamination of these two mycotoxins in 40 of 165 samples of rice, in 22 of 27 samples of rice husk, in 14 of 19 samples of rice bran, and in eight of 18 samples of broken rice (Table 6). Co-occurrence was observed for AFT and ZON in 17%, AFT and OTA in 24%, AFT and CTV in 6%, OTA and CTV in 5%, and CTV and ZON in 3% of the rice samples. The highest levels of contamination were found in rice sub-products or derived products in the husk and rice bran. These fractions were also the major contributors for co-occurrence. The co-occurrence of DON and other mycotoxins was not observed since the great majority of the samples showed no contamination by this toxin (Table 6). Rosa et al. (2010) found five samples positive for CTV, and among them were three samples of rice, with concentrations ranging from 12 to 96.7 mg kg 1, and two were from bran, with concentrations from 128 to 254 mg kg 1. The results found by Rosa et al. were higher than those found in the present paper, where the highest level of CTV contamination was 51.0 mg kg 1 in the bran fraction and 31.1 mg kg 1 in rice samples (Table 4), but in both studies the highest levels of contamination were present in the CTV bran fraction. There are not many recent studies available for comparison with the results found in this paper. JECFA, considering the carcinogenic potential of OTA, ZON and DON, established a provisional tolerable weekly intake (PTWI) of 0.1, 0.5 and 7.0 mg kg 1 bw (body weight) per week, respectively. Considering the daily intake of about 100 g of rice, and calculating a provisional tolerable daily intake (PTDI), a sample of rice contaminated with OTA, ZON and DON, at levels of contamination similar to the mean, 0.5, 42.47 and 119.33 mg kg 1, respectively (Table 7), contributes 5.91%, 99.1% and 19.9% for a person with a body weight of 60 kg, and comparing PTDI with the maximum contamination, contributes 352.8%, 11 369.2% and 40.7% (Table 7).

The results found show the necessity of monitoring rice production in Brazil. A subsequent study is also needed to evaluate the synergism of these mycotoxins. The levels of contamination determined for CTV in rice samples tested were not high enough to justify the occurrence of beriberi in the state of Maranha˜o. However, the contamination levels determined for AF total and ZON are worrying. It can also be observed that rice processing contributes to reducing the levels of contamination in polished rice because the lowest levels of contamination were observed in this fraction.

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