Nanofiltration of Aqueous Propolis Extracts and the Effects of Temperature, Pressure and Ph in the C

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Nanofiltration of Aqueous Propolis Extracts and the Effects of Temperature, Pressure and Ph in the Concentrated Product Beatriz C. B. S. Mello1,3*, José Carlos C. Petrus2, Miriam Dupas Hubinger3 School of Food Engineering, Federal University of São João del Rei, P.O. Box 56, Sete Lagoas, MG, 35701-970, Brazil.

2Department of Food Chemistry and Engineering, Federal University of Santa Catarina, P.O. Box 476, Florianópolis, SC, 88040-900, Brazil 3Department of Food Engineering, School of Food Engineering, University of Campinas, P.O. Box 6121, Campinas, SP, 13083-862, Brazil 1

*biacbs@gmail.com Abstract Propolis is a natural product with high concentration of polyphenolic compounds that is responsible for its antimicrobial and antioxidant effects. In order to establish the best parameters to obtain aqueous propolis extract with the highest contents of flavonoids and polyphenols, a response surface methodology was used in this work, in a factorial experiment 23 totalizing 17 tests, where the independent variables were feed pH (4.3- 7.7), temperature (23-57°C), and pressure (5.3-8.7 bar). The extract was processed until a volume reduction factor (VRF) of 4.0 was reached. The response surface models were tested with analysis of variance (ANOVA) and the maximal output responses have been predicted and confirmed experimentally.

The optimal operational conditions determined were feed pH 7.7, temperature of 35°C, and pressure of 8.7 bar. Under these optimal conditions, a permeation flux of 17.7 L/(h.m2) and a retention of 96% for flavonoids and 94% of polyphenols were obtained. Mathematical models fitted to experimental data suggested that cake layer formation is not predominant in aqueous propolis nanofiltration while the fouling caused by pore blocking was the main factor to permeation flux decay.

Keyword Nanofiltration; Propolis; Flavonoids; Phenolics; Central Composite Design

Introduction Propolis (sometimes also referred to as bee glue) is a resinous natural product, produced by bees (Apis mellifera), from vegetable parts and secretions. Bees use it mainly as a sealer to cover the hive interior and to repair fissures as well as to protect against pathogen microorganisms. Propolis is normally composed of 45% resins, 30% waxes and fatty acids, 10% essential

oils, 5% pollens, and 10% organic compounds and minerals (Bankova, Castro & Marcucci, 2000; Krell, 1996)). More than 300 compounds, among them flavonoids aglycones, phenolic acids and their esters, phenolic aldehydes, alcohols, ketones, terpenes, steroids, sugars, and amino acids have been detected in raw propolis, but the proportion of each compound differs greatly with botanical and geographical factors, as well as the collection season (Marcucci, 1995; Park, Alencar, & Aguiar, 2002; Chailou & Nazareno, 2009).

The compounds present in propolis are the explanation for some of its biological activities as antimicrobial and antioxidant ones which are responsible for the great interest by the pharmaceutical industry and healthfood stores, being used in foods, beverages, cosmetics, and medicine to improve health and prevent diseases (Gülçin, 2010; Functional foods, 2009; Miguel, Nunes, Dandlen, Cavaco & Antunes, 2010).

Propolis extracts have been used since ancient times as a medicine because of their biological properties such as antibacterial, antifungal, antiprotozoan, antiviral, immunomodulatory, anti-inflammatory, antioxidant, and antitumor activity (Marcucci, 1995; Gülçin, 2010; Miguel, Nunes, Dandlen, Cavaco & Antunes , 2010; Choi et al., 2006; Kujumgiev et al., 1999). The extracts are normally obtained by an ethanol-water mixture (Park, Ikegaki, Abreu, & Alcici, 1998). However, ethanol causes some disadvantages to the final product as, for example, its high residual flavor, a few adverse reactions and cultural restrictions to alcohol consumption (Konishi, Sawaya, Custódio, Cunha, & Shimizu, 2004). Some other solvents have been tested in order to replace ethanol as for example, propylene 55

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Studies in Chemical Process Technology (SCPT) Volume 1 Issue 4, November 2013

glycol (Bonvehí & Gutierrez, 2012), water (Nagai, Inoue, Inoue, & Suzuki, 2003), water with tensoactives (Konishi, Sawaya, Custódio, Cunha, & Shimizu, 2004), etc. Among all these possibilities, a potential solvent is alkaline water that was efficient in the extraction of functional compounds from propolis, which is cheap and results in a final extract with high antioxidant activity and antimicrobial activity against Staphylococcus aureus (Mello & Hubinger, 2012).

Furthermore, for industrial applications, the solvent used for the extraction is often needed to be reduced or removed. The traditional methods for concentrating the biologically active compounds from propolis include simple steam distillation, vacuum distillation, lyophilization, or evaporation (Krell, 1996), which generally requires an increased temperature and high energy consumption. High temperatures are inappropriate for heat-sensitive products as phenolic compounds, resulting in compounds losses due to degradation.

A membrane separation process can be utilized as an alternative to these conventional processes.

Nanofiltration has been used for separation and concentration of substances with molecular masses lower than 1000 g/mol as it is a process with reduced operational cost that can be performed at ordinary temperature (Peshev, Peeva, Peev, Baptista, & Boam, 2011; Mello, Petrus, & Hubinger, 2010; Tylkowski et al., 2010; Cissé, Vaillant, Pallet, & Dornier, 2011). The viability of the membrane concentration process depends on the process conditions, membrane properties, solute-membrane affinity, temperature, and pressure. Correct control of these variables can minimize some effects that reduce permeation flux like gel layer cake formation or fouling and can increase the process efficiency (Pilipovik & Riverol, 2005; Ushikubo, Watanabe, & Viotto, 2006; Pap, et al., 2012).

Therefore, nanofiltration is an attractive alternative concentration method since it enables solvent reuse, the concentration at low temperatures, and energy saving. In propolis extracts, the content of bioactive compounds and the high solvent to solid extraction ratio are the main factors to explain the importance of membrane concentration of the extracts. The main disadvantages of this technique is the fouling occurrence and the high pressure used during the process (Cissé, Vaillant, Pallet, & Dornier, 2011; DíazReinoso, Moure, Domínguez, & Parajó, 2011).

Response surface methodology has been used to analyze the effects of multiple factors and their

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interactions with the objective to optimize process variables, making the overall process more feasible (Ruby-Figueroa, Cassano, & Drioli, 2012). In membrane processes, it can be cited the selection of the best process condition in nanofiltration of aqueous mate extracts (Murakami et al., 2011), in wastewater ultrafiltration (Yuliwati, Ismail, Lau, Ng, Mataram, & Kassim, 2012), in extraction of red pigments from bayberry pomace (Zhang, et al., 2012) and during nanofiltration of soybeans extracts (Benedetti, Prudêncio, Mandarino, Rezzadori, & Petrus, 2011).

The aim of this work was to apply the response surface methodology approach to optimize the nanofiltration process conditions (extraction pH, temperature, and pressure) for aqueous propolis extracts concentration, verifying the quality of the concentrated products in terms of flavonoids and phenolic compounds retention and process permeation flux. A quadratic polynomial model was used to correlate the responses with the process variables. Based on the obtained results, the best conditions were chosen and a new run was made in order to validate the method. Permeation flux curve was compared to some mathematical models to predict the main factors that cause the flux decay.

Material and Methods Material Raw propolis was obtained from Apis mellifera beehives in the State of São Paulo, Brazil, and was acquired in a single batch, in order to minimize the variability associated with the vegetation used for its production and the weather conditions. It was stored under refrigeration (4°C) until use in the preparation of extracts.

A spiral wound membrane module with an effective permeation area of 1.2 m2 with a molecular cut-off (MWCO) ranging from 150 to 300 g/mol made from polyamide and polissulfone (GE Osmonics, Philadelphia, USA) was used. For the spectrophotometrical analysis of total polyphenols and flavonoids, gallic acid and quercetin standards were used; aluminiun nitrate, sodium carbonate, potassium acetate and Folin Ciocalteau reagent were supplied by Sigma-Aldrich Corporation. (St. Louis, MO) and ethanol (99.9%) was supplied by Synth (Diadema, Brazil).

Propolis Extracts Extracts were prepared from crude propolis previously comminuted in a bench blender with a 500W motor, homogenized, weighed on a semi-

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analytical balance and mixed with distilled water in proportion of 15 mL/g propolis. The mixture was kept at room temperature for 5 days, manually stirred and pH corrected (to the pH of each experiment) with NaOH 1.0M once a day. After this period, the sample was centrifuged (Beckman- Allegra 25-R, Beckman Coulter, Munich, German) at 8800 g for 20 min. The supernatant was filtered with paper filter (Wathman n°1) and the resulting extract was stored at room temperature in a closed recipient in the dark.

Nanofiltration Assays Nanofiltration trials have been performed using a GE EZ2 Kit, 60 Hz (GE Power & Water, Pennsylvania, USA) schematized in Figure 1. The feed solution was circulated through the membrane module with the driven force provided by the positive displacement pump, where the system pressure and the volumetric flow rate can be adjusted using the concentrate outlet valve.

In order to measure the permeation flux, the experiments were carried out in closed system (permeation and concentrate were recirculated) in the conditions of pressure and temperature (controlled through a jacket tank) established in the central composite design (Table 1). Permeation aliquots were taken off for 10 seconds after 1, 5, 10, 20, 30, 40, and 50 minutes of process and the permeation flux (J) (L h-1 m2) was calculated according to equation 1.

J=

Vp t

â‹… Ap

(1)

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where Vp(L) is the amount of permeation collected during the period of time t (h) and Ap (m2) is the permeation area of the membrane.

After that, the system was operated in open form, without permeation recirculation until reaching a volume reduction factor (VRF) of 4. VRF was calculated as the ratio between the initial volume (L) of the aqueous propolis extracts in the feed and the final volume (L) of the concentrate from nanofiltration process. In conclusion, the process was operated until the concentrate volume was 25% of the initial solution.

After each run, the equipment was cleaned with alkaline solution (0.1% NaOH) according to manufacturer's instruction. The flux after cleaning was compared to the initial values for distilled water in order to assure the cleaning efficiency.

Experimental Design A central composite design was used to identify the relationship among three independent process variables and the dependent responses. The independent variables were temperature (X1), pressure (X2) and pH of feed solution (X3) that had been analyzed through 17 assays: eight factorial, six axial, and three at a central point (performed to estimate possible pure errors). All the experiments were carried out at random to minimize systematic errors. The dependent variables were the total polyphenols (Y1) and flavonoids (Y2) in the concentrate and the permeation flux (Y3). Operating ranges and the levels of the considered variables are given in Table 1.

FIGURE 1 SCHEMATIC DIAGRAM OF THE NANOFILTRATION UNIT.

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TABLE 1 REAL AND CODED VALUES OF THE EXPERIMENT VARIABLES USED FOR THE EXPERIMENTAL DESIGN 2

Independent variables

3

Real values and coded levels -1.68

-1

0

+1

+1.68

Temperature (°C)

23

30

40

50

57

Pressure (bar)

5.3

6.0

7.0

8.0

8.7

Feed pH

4.3

5.0

6.0

7.0

7.7

0,5   1 J = J 0  1+ ⋅ k s ( A ⋅ J 0 ) ⋅ t   2 

The pH levels were established based on a previous work of Mello & Hubinger (2012), who studied propolis extraction with different pHs and solvents, reaching better products in terms of polyphenol composition for aqueous extracts at basic pH. In that work, it was verified that the pH extraction without any modification was 4.3, so this value was chosen to be the start condition. Akowuah, Mariam, & Chin (2009) reported that the total phenol levels in Gynura procumbens leaf extracts decreased for temperatures higher than 40ºC. Therefore, in this work the average operational temperature was 40ºC. Experimental data were fitted to the following second order polynomial model and regression coefficients were obtained. The generalized second-order polynomial model proposed for the response surface analysis was given by equation 2 where β0, βi, βii, βij are regression coefficients for intercept, linear, quadratic and interaction terms, respectively. Xi and Xj are coded values of the independent variables.

3

3

i= 1

i= 1

2

Y= β 0 + ∑ βi X i + ∑ βii X i2 + ∑

3

∑

i = 1 j = i +1

βij X iY j

(2)

The experimental results for polyphenols and flavonoids in the concentrated product were expressed as mean ± standard deviation. Statistical analysis was performed by using Statistica© 8.0 (Statsoft Inc, Tulsa, Oklahoma) software and fitted to a second order polynomial regression model containing the coefficient of linear, quadratic and interaction terms. The significance of all terms in the polynomial regression was statistically analyzed by computing the F-value at a probability (p) of 0.10.

Mathematical Modeling The fouling mechanisms in membrane surface are widely used to predict the variation in permeation flux with crossflow filtration time (Díaz-Reinoso, Moure, Domínguez, & Parajó, 2011; Hermia, 1982; Vela, Blanco, Garcia, & Rodriguez, 2009; Rezaei, Ashtiani, & Fouladitajar, 2011): total pore blocking model (Eq. (3)), standard pore blocking model (Eq. (4)), intermediate pore blocking model (Eq. (5)), and cake layer

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formation model (Eq. (6)). The permeation flux (J) was fitted to the different models, correlating the initial permeation flux (J0), the operational time (t) and the membrane area (A) with the respective (k) constants. −k t J = J0e (3)

J = J 0 ⋅ (1+ kt ⋅ A ⋅ J 0 ⋅ t )

(

−2

−1

J = J 0 ⋅ 1+ 2k c ⋅ ( A ⋅ J o ) ⋅ t 2

(4) (5)

)

 −1     2 

(6)

Extracts Analysis 1)

Determination of Density and Viscosity

Propolis extracts were analyzed by a 25 mL picnometer previously calibrated with distilled water at 25°C. The water density at 25°C was 997.08 kg/m3 and its viscosity was 8.90.10-4 Pa.s (Perry & Chilton, 1980).

The extracts viscosity was determined through the use of a Canon-Fenske viscometer. This instrument is appropriated to measure viscosity of Newtonian fluids, by the use of the equation 7. μ (7) = K .t ρ Where: μ = viscosity (Pa.s); ρ = density (kg/m3); K = viscometer constant (1.44.10-8 m2/s2); t = flow time.

2)

Determination of Total Flavonoids

The total flavonoid content of the propolis solutions was determined by the aluminum complexation method (Marcucci, Woisky, & Salatino, 1998). In this procedure, the extracted solutions were diluted with deionized water in the proportion of 1:10 (0.5 mL) and mixed with 0.1 mL of 10% aluminum nitrate, 0.1 mL of 1 mol/L potassium acetate and 4.3 mL of 80% ethyl alcohol. The samples were kept at room temperature for 40 min and the absorbance was read at 415 nm in a spectrophotometer (UNICO-2800, UNICO Sci., New Jersey, USA).

Quercetin was used as the standard to produce the calibration curve. The mean of three readings was used and the total flavonoid contents were expressed in mg of quercetin equivalents (mg/g).

3)

Determination of the Phenolic Compounds

The polyphenols in the propolis solutions were determined by the Folin-Ciocalteau colorimetric method (Kumazawa, Hamasak, & Nakayama,

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coefficients for the linear, quadratic and interaction terms were determined through multiple linear regressions.

The significance of each regression coefficient was determined statistically by computing the t-value from the pure error obtained from the replicates at the central point of the experiment. ANOVA was used to validate the model. The regression coefficients were used to generate response surfaces by software Statistica 8.0 (StatSoft Inc., Tulsa, OK, USA).

2004). According to this procedure, the extracted solution was previously diluted with deionized water in the proportion of 1:10 (0.5 mL) and mixed with 0.5 mL of the Folin-Ciocalteau reagent and 0.5 mL of 10% Na2CO3. The absorbance was read at 760 nm in spectrophotometer (UNICO-SQ-2800, UNICO Sci., New Jersey, USA) after 1 hour of incubation at room temperature. Gallic acid was used as the standard to produce the calibration curve. The mean of three readings was used and the total phenolic contents were expressed in mg of gallic acid equivalents (mg/g).

Results and Discussion Effects of pH in the Feed Solutions

Statistical Analysis

All the feed extracts used in the experimental design were analyzed according to its density and viscosity characteristics and for their flavonoids and phenolic compounds composition. These data are shown in Table 2.

The data were expressed as means and standard deviation. Analysis of variance (ANOVA) and Tukey's studentized range test (5% significance) were carried out to detect any significant differences. The regression

TABLE 2 DENSITY, VISCOSITY, FLAVONOIDS AND POLYPHENOLDS CONTENTS OF PROPOLIS EXTRACTS PREPARED WITH DIFFERENT PHS.

Density (Kg/m3)

Viscosity (Kg/m.s)

Flavonoids*

Phenolic Compounds**

pH 4.3

1002.3 ± 0.6 (a)

(10.15± 0.06)10-4 (a)

20.93 ± 1.26 (a)

102.37 ±1.75 (a)

pH 5.0

1001.8 ± 0.5 (a)

(10.07 ± 0.02)10 (a)

21.52 ± 0.37 (a)

102.70 ± 0.11 (a)

pH 6.0

1000.9 ± 0.4 (a)

(10.07 ± 0.02)10-4 (a)

25.87 ± 0.69 (a)

103.32 ± 2.09 (a)

pH 7.0

1017.5 ± 1.9 (b)

(13.05 ± 0.02)10-4 (b)

60.99 ± 3.98 (b)

120.94 ± 1.46 (b)

pH 7.7

1047.9 ± 1.3 (c)

(28.19 ± 0.11)10-4 (c)

89.14 ± 0.81 (c)

138.45 ± 2.37 (c)

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Values are represented by mean ± S.D. of three experiments * quercetin equivalents ** gallic acid equivalents Means with different letters within a column are signicantly different at p<0.05 TABLE 3 VARIABLE LEVELS AND RESPONSES OF FLAVONOIDS, PHENOLIC COMPOUNDS AND PERMEATION FLUX BASED ON TEMPERATURE, PRESSURE AND FEED PH VARIATION.

Run

Temperature (°C)

Pressure (bar)

pH

Flavonoids*

Phenolic Compounds**

Flux(L h-1 m-2)

1

30 (-1)

6.0 (-1)

5.0 (-1)

23.63 ± 2.27

90.85 ± 2.45

25.80

2

50 (+1)

6.0 (-1)

5.0 (-1)

10.59 ± 1.75

89.27 ± 1.48

49.50

3

30 (-1)

8.0 (+1)

5.0 (-1)

12.69 ± 1.19

92.17 ± 2.16

40.50

4

50 (+1)

8.0 (+1)

5.0 (-1)

7.17 ±1.47

88.26 ± 1.56

70.50

5

30 (-1)

6.0 (-1)

7.0 (+1)

15.66 ± 1.05

127.59 ± 1.76

24.30

6

50 (+1)

6.0 (-1)

7.0 (+1)

9.54 ± 0.95

78.48 ± 1.42

43.80

7

30 (-1)

8.0 (+1)

7.0 (+1)

9.62 ± 0.92

105.10 ± 2.26

36.00

8

50 (+1)

8.0 (+1)

7.0 (+1)

2.74 ± 0.97

73.55 ± 1.17

61.20

9

23 (-1.68)

7.0 (0)

6.0 (0)

30.17 ± 4.64

89.57 ± 1.42

25.50

10

57 (+1.68)

7.0 (0)

6.0 (0)

4.16 ± 0.95

70.47 ± 0.87

66.60

11

40 (0)

5.3 (-1.68)

6.0 (0)

27.52 ± 2.85

84.85 ± 1.62

36.00

12

40 (0)

8.7 (+1.68)

6.0 (0)

6.69 ± 1.18

85.17 ± 1.26

57.90

13

40 (0)

7.0 (0)

4.3 (-1.68)

21.33 ± 1.96

72.63 ± 0.99

27.30

14

40 (0)

7.0 (0)

7.7 (+1.68)

21.91 ± 1.54

129.47 ± 2.16

32.10

15

40 (0)

7.0 (0)

6.0 (0)

11.43 ± 1.02

83.40 ± 2.24

49.20

16

40 (0)

7.0 (0)

6.0 (0)

15.18 ± 1.37

84.21 ± 3.12

48.90

17

40 (0)

7.0 (0)

6.0 (0)

16.27 ± 1.95

83.36 ± 2.10

51.30

* quercetin equivalents ** gallic acid equivalents

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TABLE 4 MODEL REGRESSION COEFFICIENTS AND FTEST FOR PHENOLIC COMPOUNDS, FLAVONOIDS AND PERMEATION FLUX TO CODED INDEPENDENT VARIABLES.

Source

Phenolic Compounds (mg/mL)

Mean

83.39*

14.77*

49.57* 12.27*

Flavonoids (mg/mL) Permeation Flux( L/(h.m2))

Temperature

-17.32*

-11.03*

Temperature x Temperature

-0.72

-1.27

-0.53

Pressure

-3.89

-9.11*

7.44*

Pressure x Pressure

2.80

-1.31

-0.21

pH

17.54*

-2.27

-0.95

PH x pH

14.14*

1.87

-6.31*

Temperature x Pressure

3.80

1.69

1.50

Temperature x pH

-18.79*

1.39

-1.12

Pressure x pH

-6.93

0.38

-0.82

R

0.81

0.67

0.95

Ftest

36.65

14.28

147.87

Flisted

3.26

3.26

3.26

2

*Statistically significant values (p< 0.10).

From these data, it was possible to note that both density and viscosity decay up to pH 6.0, but without any significant difference among the samples. At pHs 7.0 and 7.7, the values increased, which can be an evidence of a better extraction of flavonoids and polyphenols. These compounds show a higher molar weight than the solvent, increasing solution density.

This hypothesis can be confirmed when the data for flavonoids and polyphenols are examined; only the samples at pHs higher than 7.0 showed a significant difference among the other with lower pHs. Extract with pH 7.7 showed an increase of almost 40% in phenolic compounds and the total amount of flavonoids was six times higher than the extract without pH modification (4.3).

Central Composite Design Results The solutions at pH 4.3, 5.0, 6.0, 7.0 and 7.7 were subjected to the concentration process according to the conditions established in Table 1. The responses (total phenolic contents, flavonoids and permeation flux) of each run of the experimental design are presented in Table 3. Permeation flux indicates the flux obtained in the open system after 20 minutes of process.

In the experiments at pH 5.0, increasing process temperature from 30 to 50°C, the permeation flux was 20% higher when a 6.0 bar pressure was applied and 78% higher at 8.0 bar. In the same way, for the pressure increase from 6.0 to 8.0 bar, the flux was 50% higher at 30°C and 40% higher at 50°C. The data from the runs 15, 16, and 17 (40°C, 7.0 bar and pH 6.0) proved that the process was well reproducible, since the results were similar in the central point.

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The run 10 (57°C, 7.0 bar and pH 6.0) resulted in the concentrated solution with the lowest contents of functional compounds, but the concentration in permeation was lower too. In this way, the compounds were not lost to permeation but a degradation of flavonoids and phenolic compounds could have happened, due to the high process temperature. In run 13 (40°C, 7.0 bar and pH 4.3) a higher loss of flavonoids to permeation line has occurred, compared to the other runs. The lower compounds concentration in feed solution can be responsible for a low polarization layer formation that can act as a second barrier to the compounds, increasing the membrane permeation. Total phenolic contents in concentrated propolis extracts varied from 9.81 to 117.45 gallic acid equivalents (mg/mL) while the flavonoids contend varied from 27.46 to 301.69 quercetin equivalents (mg/mL). Permeation flux ranged from 24.30 to 66.60 L/h.m2. ANOVA was used to estimate the statistical significance of the factors and interactions between them. Regression coefficients and analysis of variance for total phenolic compounds, flavonoids, and permeation flux are summarized in Table 4.

In the case of phenolic compounds, the statistically significant parameters (p<0.10) were mean, linear temperature, linear pH, and the interaction between temperature and pH, while for flavonoids, the significant parameters in the same statistical conditions were mean, linear temperature and linear pressure.

For permeation flux, mean, linear model parameter of temperature and pressure and quadratic model for pH were statistically significant for this response (p < 0.10).

Analysis of variance (ANOVA at p<0.10) considering

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only the statistically significant regression coefficients showed that permeation flux and phenolic compounds presented a Ftest higher than the Flisted and the regression coefficients were higher than 0.80, allowing the construction of models and response surfaces. For flavonoids contents, although the Ftest values were higher than the Flisted, the R2 values were lower than 0.75, not allowing describing the results using models and response surfaces.

The coded models proposed to describe the permeation flux and phenolic compounds in concentrated product are obtained by Equations (8) and (9).

Permeate Flux = 48.84 + 24.53 ∗ T +14.88 ∗ P − 12.28 ∗ pH 2 (8) Phenolic Compounds = 84,41-17,32 ∗ T + 17,54 ∗ pH + 13,67 ∗ pH 2 - 18,79 ∗ T pH

(9)

Where: T is temperature; P is the pressure and pH is the pH in feed solution. Coded independent variables ranged from -1.68 to 1.68.

The response surfaces were generated to verify the interaction between two of the independent variables of propolis extracts nanofiltration, while the third variable was fixed at the central point (Figure 2).

Phenolic compounds contents were expressed in gallic acid equivalents.

By analyzing Figure 2(a), it is possible to note that the bigger permeation flux values occurred with high pressure and for pHs between 5.0 and 7.0. It was expected that increasing the pressure, the flux would

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increase in a proportional way (Labanda, Vichi, Llorens, & López-Tamames, 2009), for the pressure being the main process driven force, responsible for solute permeation through membrane. The extracts with pH between 5.0 and 7.0 were those with lower viscosities, which can explain the easy membrane permeation and higher flux. The permeation flux was directly proportional to temperature (Figure 2(b)), since this variable is normally related to viscosity solution. Higher temperatures cause a decrease in the solution viscosity, becoming easier to permeation through the system.

More basic feed solution resulted in a concentrate with higher phenolic contents, as can be seen in Figure 2(d).

This behavior was expected, as aqueous propolis extracts prepared in basic pHs have a higher polyphenols concentration that were retained by the membrane. By analyzing Figure 2(e) it was possible to remark that the more favorable conditions for phenolic concentration are low temperature and high pHs, and that pressure do not affect the final product. Figure 2(f) represents the phenolic compounds in concentrated aqueous propolis extract according to process temperature and pressure, where it is possible to note that phenolic contents are lower when the temperature increases, while the pressure did not cause any variation in the phenolic composition. Therefore, the optimal variables range for phenolic contents are temperature of 23-35°C and pH of 6.5-7.7 for all pressures range.

FIGURE 2 RESPONSE SURFACES FOR PERMEATION FLUX AND PHENOLIC COMPOUNDS AS A FUNCTION OF: PH AND PRESSURE (A) AND (D), PH AND TEMPERATURE (B) AND (E), PRESSURE AND TEMPERATURE (C) AND (F).

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FIGURE 3 TIME PROFILES OF THE PERMEATION FLUX OF THE AQUEOUS PROPOLIS EXTRACTS NANOFILTRATION. LINES REPRESENT THE CALCULATED VALUES ACCORDING TO THE FOULING MODELS AND POINTS REPRESENT THE EXPERIMENTAL DATA OBTAINED AT 8.7 BAR, 30°C AND PH 7.7.

From all the data obtained in the central composite design – Table 4 and Figure 2- it was possible to choose the best variables for aqueous propolis extracts concentration. Temperature was fixed at 30ºC that was the highest possible temperature without phenolic compounds and flavonoids degradation. Feed at pH 7.7 generated an extract with high concentration of the functional compounds and a pressure of 8.7 bar was the maximum operational pressure which have generated the highest permeation flux by experimental design. In order to validate the chosen conditions, a new extract was prepared and concentrated until a volume reduction factor equal to 4.0. Permeation flux responses calculated by mathematical methods and the experimental data obtained in open system are showed in Figure 3. According to the curves, the best prediction of filtration flux was achieved with intermediate pore blocking, internal pore blocking and total pore blocking models, with regression coefficients of 98, 98, and 96%, respectively. In these models, the initial flux decay was not remarkable, which gives a best fit to the experimental data. The curve obtained considering the cake layer formation did not fit well the experimental data showing a R2 = 78%. These results could suggest that cake layer formation is not predominant in aqueous propolis nanofiltration while the fouling caused by pore blocking (all cases) is the main factor to permeation flux decay. However, since both fouling models give similar flux decay and both models adjust experimental data well, it could be assumed that the

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fouling phenomenon is not very pronounced under the working tested conditions.

Permeation flux obtained after the stabilization in a closed system was 17.7 L/(h.m2) and its behavior was very similar to the typical permeation flux curves, as those seen in red wine concentration (Banvolgyi, Kiss, Bekassy-Molnar, & Vatai, 2006) and grape waste extracts (Diaz-Reinoso, Moure, Dominguez, & Parajó, 2009). During the first 5 minutes of the trials, flux decay had occurred due to the polarization concentration in the membrane surface and fouling in the pores. After that, the permeation flux was constant until the end of the process.

Tylkowski et al. (2010) tested propolis biologically active compounds nanofiltration in a small equipment that simulated tangential flow and noted a low sedimentation at the membrane surface that has not affected permeation flux during the process. Other works in literature showed a similar value of permeation flux as can be seen in alcoholic corn extract purification to xanthophyll production (10.0 L/h.m², at 27 bar, and 50°C) (Tsui & Cheryan, 2007), in anthocyanins concentration from aqueous blackcurrant extracts (17.3 L/h.m² at 1.4 bar, and 18°C) (Hossain, 2003) and in red wine concentration through nanofiltration (20.0 L/h.m² at 20 bar, and 50°C) (Banvolgyi, Kiss, Bekassy-Molnar, & Vatai, 2006). In the present work, the applied pressure was relatively low when compared to nanofiltration trials found in literature, but resulted in a similar or even superior permeation flux compared

Studies in Chemical Process Technology (SCPT) Volume 1 Issue 4, November 2013

to these other process, which is an evidence of its viability.

The optimized formulation was characterized according to total phenolic and flavonoids compounds and the process permeation flux was measured. These values were compared with those obtained by the use of equations 8 and 9 that represent the model equations from surface responses. In order to verify the validity of the models (Table 5), the relative deviation was calculated. The differences observed between experimental and predicted data were less than 20% for permeation flux and almost 10% for phenolic compounds. Flavonoids were not compared because it was not possible to build a model with the data obtained in experimental design.

TABLE 5 VALIDATION PROCEDURE OF PROPOLIS NANOFILTRATION ACCORDING TO THE OPTIMIZED FORMULATION.

Analysis

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