EXTRACTION AND OPTIMIZATION OF DRUMSTICK TREE (MORINGA OLEIFERA) SEED OIL BY MICROWAVE-ASSISTED EXTR

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EXTRACTION AND OPTIMIZATION OF DRUMSTICK TREE (MORINGA OLEIFERA) SEED OIL BY MICROWAVE-ASSISTED EXTRACTION METHOD USING RESPONSE SURFACE METHODOLOGY Raphael Eze Nnam*1, Chidubem Theresa Chukwu2, Mabel Chidimma Nwachukwu3, Richard U. Omonu4, Kingsley O. Okpara5, Kenneth C. Asadu6 *1Department

of Food Technology Akanu Ibiam Federal Polytechnic, Unwana Ebonyi State Nigeria. of Food Technology Akanu Ibiam Federal Polytechnic, Unwana Ebonyi State Nigeria.

2,3,4,5,6Department

ABSTRACT This research is geared towards extraction and optimization of oil from Drumstick Tree (Moringa oleifera) seed using microwave-assisted extraction method and optimize the process through (RSM) Response Surface Methodology. From the research result gotten, the microwave-assisted method of extraction was effective and efficient in extracting the oil. This was seen from the fact that the solvent used in the extraction was minimized and the time of extraction considerably shorter more than that of the conventional methods of extraction. The optimal yield of the process which was 44.33% was gotten at microwave irradiation power of 540W, irradiation time of 8 min and solute/solvent ratio of 1:30. The predicted values obtained from RSM were observed to be close to the actual experimental values as the residual difference was minimal. Thus, the Response Surface Methodology as a statistical tool was very effect in optimizing the extraction process and therefore can be used to predict oil extraction processes.

Keywords: Moringa olifera;, oil extraction; microwave-assisted; oil optimization.

I.

INTRODUCTION

Throughout history, nature provides natural food and medicines used for the treatment of a broad spectrum of diseases and for consumption to enable man live healthy. Majorly or prominently, plants have been important sources of biologically active substances for man’s nourishment and cure for various ailments (Cragg et al., 2014; Tiwari 2008; Elfahmi et al., 2014; Moloney 2016). Yati Vaidya, et al., (2015) noted that medicinally, plants have been widely used as an ingredient for the treatment of different human and animal disease because they contain high therapeutic value. Accordingly, the World Health Organization (WHO) reported that over 80% of the world population in one way or the other depends on traditional medicine that uses medicinal plants as biological source of active compounds for their primary care as well as sources of daily and healthy sustenance (Calixto 2005; WHO 2008; Prabhadevi,et al., 2012; Newman et. al., 2012). These assertions of plant can be seen from the extraction and characterization of several active phyto-compounds from these plants that have given birth to several high activity profile drugs (Mandal, et al., 2007) as well as products packaged as either food products or food supplements (Ramachandran et al 1980).

Herbal medicines have been evolving side by side of human culture. Plants are considered as natural factories for production of various phytochemicals. Many secondary metabolites like alkaloids, phenolics and flavonoids are synthesized by plants in addition to compounds that are needed for the reproduction and growth of plants.

Advancements in natural sciences led researchers towards identification and isolation of different bioactive phytochemicals. Aromatic plants have been known for a very long time and the use of them in the food and perfume industry have a long history. Among the aromatic plant species, the genus Drumstick Tree (Moringa oleifera) consists of more than 14 species in the world (Moradalizadeh et al., 2013; Ojewumi et al., 2018; Boukandoul et al., 2010). Moringa (Moringa oleifera Lam) is a type of local medicinal Indian herb which has turn out to be familiar in the tropical and subtropical countries. According to Oluwafunke (2019), Moringa oleifera lamarch belong to the genus Moringaceae with fourteen species and according to Pinto et. al., (2015) it is a multipurpose tree that is widely used in food and feed industry for various purposes.

As a plant, all the parts of Moringa oleifera i.e. the leaves, seeds, roots and flowers are suitable and can be used as food or for other beneficial applications for both human and animal consumption (Leone et al., 2016; Premi and Sharma 2013). In Africa, Moringa oleifera is a traditional food plant item and has the potential to improve nutrition, boost food security, and enhance rural development through improving and sustaining rural households as well as supporting sustainable land use and care (Eman and Muhamad 2016). The press cake, gotten after the oil extraction processes, is used as a soil conditioner and for water treatment. It is also used as source of fuel; as an intercrop with other crops and the wood pulp can be used by the mill industry for making of paper. The green

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pods, fresh and dried leaves are used as vegetable (Folkard et al 2004). Medically, all parts of Drumstick tree are used in different traditional medicines. It is basically used as a micronutrient powder to treat various ailments and diseases such as body pains, fever, asthma, cough, blood pressure, arthritis, diabetes, epilepsy, wound, and skin diseases (Gopalakrishnan et. al., 2016; Lalas et. al., 2012; Ogbunugafor et al., 2011; Orhevba et al 2013; Premi et al.

2010). Research has proven that Moringa contains many essential nutrients such as protein, vitamins, amino acid, beta-carotene, anti-inflamatory, anti-oxidant, nutrients and omega 3 and 6 fatty acids (Fahey, 2005; Hsu et al., 2006; Kasolo et al., 2010). Furthermore, it has been discovered scientifically, that the leaves, which are rich in protein, minerals, β-carotene and antioxidant compounds, are used not only for human and animal nutrition but also in traditional medicine (Leone et al., 2015; Leone et al., 2015). The phenolic portion of the M. oleifera leaf extract has been proven to retard the rancidity of food products that contain fat (Sin et. al., (2014); Anwar et al.

2007; Arabshahi-D et al. 2007; Reddy et al. 2005). Therefore, Moringa having a good antioxidant property would be very useful for the food industry ((Devgun et al., 2009; Chan et al., 2011; Martin and Gilbert 1968; Osawa and Namiki 1981). The seeds of Moringa tree have attracted a lot of interest because it contains up to 40% amount of oil that has high quality composition in fatty acid (oleic acid > 70%). This oil when refined is a notable resistance to oxidative degradation (Anwar, et al 2005). The oil is commercially known as “Ben oil” or “Behen oil”. Its properties make it suitable for both human consumption and commercial purposes. Leone et al., (2016) noted that Moringa oil can conveniently and adequately replace olive oil in the diet as well as for non-food purposes such as biodiesel, cosmetics, and a lubricant for fine machinery. More so, the by-product (seed cake) of the oil extraction process can be used in waste water treatment as a natural coagulant (Ndabigengesere, A. & Subba Narasiah 1998) or as an organic fertilizer to improve agricultural productivity (Emmanuel et al., 2011). According to Palafox et al.

(2012), Moringa oil is more suitable for frying than canola oil, soybean oil, and palm oil.

Depending on physical nature and the properties of phyto-constituents, various methods are in use to obtain the crude extract. Among these various conventional extraction methods including infusion, digestion, decoction, percolation and maceration are commonly practiced in herbal industry for crude extraction (Simha et al., 2016).

Extraction has shown to be the first basic step in medicinal plant research because the isolation and purification of chemical constituents present in plants starts when the crude extract has been gotten (Romanik et al., 2007).

Regardless of the importance of extraction in both medical research and in the food industry, the area oftentimes remains neglected (Smith 2003).

Essential oils (Eos) ordinarily called volatile or ethereal oils are aromatic oily liquids that is gotten or extracted from plant materials by physical means. The constituents of an essential oil may be classified into two principal groups namely hydrocarbons (terpenes, sesquiterpenes and diterpenes) and oxygenated compounds (Moradalizadeh et al 2013). The oxygenated compounds are gotten from alcohols, aldehydes, esters, kethons, phenols, oxides, etc (Heath, 1978). Essential oils are used extensively in the cosmetic industry to produce products like cologne waters, bathing lotions, hair lotions, shampoos, and as ingredient used for the production of disinfectants, food and insecticides (Boelens, 1985). Lis-Balchin, (1997); Reynolds, (1996) also observed that essential oils from plant have been used for thousands of years to preserve food, produce drugs (pharmaceuticals), as alternative medicine and for natural therapies.

The essential oil obtained from aromatic herbs is traditionally gotten by hydrodistillation. However traditional methods of extraction are time taking, like, 2-7 hours at least are required for maceration, and this process also requires a large amount of solvents (Regasini et al., 2008; Jiménez-Carmona et al., 1999). Luque de Castro and Garcia-Ayuso (2008); Mandal et al., (2007) noted that the traditional techniques of solvent extraction of plant materials are mostly based on the correct choice of solvents and the use of heat or/and agitation to increase the ability of the needed compounds to dissolve in the solvent and also enhance the mass transfer ability. Usually the traditional technique takes longer period of time for the extraction process to be completed thereby running the risk of having most of the phyto-constituents being thermally degraded. In the case of Soxhlet extraction, the targeted molecules might be decomposed due to high temperatures (Afoakwah et al., 2012). Khajeh et al.,(2004); Tuan and Ilangantileke (1997) stated that the major disadvantages of the traditional extraction method of oil include losses and degradation of some volatile compounds due to the long time taken in extraction, reduction or degradation of unsaturated or ester compounds due to heat or hydrolytic effects. Therefore, the need to develop an alternative extraction method that is rapid, sensitive, safe, and conserve energy is highly desirable. It should be techniques that can be automated, with shortened extraction times and reduced organic solvent consumption thereby preventing pollution and reducing sample preparation costs.

According to Moret et al., (2019), microwave energy was first used for oil extraction in 1986, when Ganzler et al.

1986, investigated the possibility of applying microwave irradiation in the extraction of different compounds from www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science

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soil, seeds, foods and feeds, before chromatographic determination. He noted that since then, many works have been done on the use of microwaves for the extraction of fats, bioactive and nutrient components, inorganic (metals) and organic contaminants, from plant material and foods. Recently, the application of microwave dielectric heating in many different processes in the chemical and food industry has been of great interest. This is in order to replace the traditional or conventional ways of extraction. Therefore microwave-assisted extraction processe is extraction done under the action of microwave selective heating (Chemat et al., 2006). Driven by reducing energy and waste water or solvent, advances in microwave extraction have given birth to a number of microwave related techniques such as microwave-assisted solvent extraction (MASE) (Ganzler et al.,1986, Lettelier and Budzinski 1999) vacuum microwave hydrodistillation (VMHD), compressed air microwave distillation (CAMD) (Craveiro et al., 1989), microwave hydrodiffusion and gravity (MHG) (Abertet al., 2008, Bousbia et al., 2009)-Vian and microwave-accelerated steam distillation (MASD) (Chemat et al.,2006, Sahraoui et al.,2008). Microwave-assisted hydrodistillation (MAHD) method also is a more recent technique used to recover volatile components (Wang et al., 2009, Golmakani et al., 2008, Rezvanpanah et al., 2008, Phutdhawong et al., 2007, Iriti et al., 2006). Microwave-assisted extraction (MAE) has been recognized as a technique with several advantages over other extraction methods as it reduces costs, extraction time, energy consumption, and CO2 emissions (Cardoso-Ugarte et al., 2012). Because of the capacity of microwave, MAE, is utilized to concentrate plant metabolites with the solvents (Devgun et al., 2009). Microwaves are part of electromagnetic spectrum of light with a range of 300 MHz to 300 GHz and wavelengths of theses waves range from 1cm to 1m (Mandal et al., 2007). These waves are made up of two perpendicular oscillating fields which are used as energy and information carriers. First application of microwaves includes its interaction with the desired materials which can absorb a part of its electromagnetic energy and can convert it into heat. Commercial the frequency of energy used by microwaves for this purpose is 2450 MHz. This frequency is almost equivalent to 600-700W (Afoakwah et al., 2012).

In its practical sense, microwaves induce dipole rotation in organic molecules as well as heating that causes the destruction of hydrogen bonding. This causes the traffic of ions which results in a heating effect due to increased kinetic energies of ions as well as friction between ions due to their continuous movements and change in directions. Destruction of hydrogen bonding also increases the penetrating efficiency of the solvents into the plant matrix (Hudaib et al., 2003; Datta et al., 2005; Tang, 2005).

Figure 1: Representation of the two heating modes: (a) by convection and (b) by microwave energy (Neas and Collins, 1988).

Therefore, this research work seeks to extract oil from M. oleifera seed using the microwave-assisted solvent extraction method; determine the physiochemical properties of the oil and optimize the extraction process using Response Surface Methodology.

II.

METHODOLOGY

a. SAMPLE COLLECTION AND PREPARATION The fully matured seeds of M. Oleifera were gathered from Unwana and processed in Department of Food Technology Akanu Ibiam Federal Polytechnic Unwan, Ebonyi state, Nigeria and carefully separated from the

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husks. The seeds were sundried for two weeks making them fit for milling. The dried seeds were milled and grounded to powder using an electric blender. b. DESIGN OF EXPERIMENT The D-optimal design tool of the Design Expert software was used to prepare the design of experiment that was used to effect the extraction process. The experimental design matrix by D-Optimal for three-level-three-factor with RSM indicating the coded factors is shown on Table 1.

Table 1: Factors and Their Levels for D-optimal Factor

Symbol

Coded Levels

Factor

-1

0

+1

Time (min)

A

4

8

12

Power (W)

B

180

360

540

Solute/solvent ratio

C

1:10

1:20

1:30

Table 2: Three-Level Factor D-Optimal Design

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Factor 1

Factor 2

Factor 3

Run

A: Time (minus)

B: Power (Watt)

C: Solute/Solvent o (Ratios)

1

12

180

1:10

2

12

540

1:20

3

4

180

1:30

4

4

180

1:10

5

8

360

1:20

6

12

180

1:30

7

4

540

1:30

8

8

360

1:10

9

12

540

1:20

10

12

360

1:10

11

4

360

1:10

12

4

540

1:30

13

8

540

1:30

14

4

540

1:10

15

12

540

1:30

16

4

180

1:30

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17

12

180

1:20

18

12

180

1:20

19

4

540

1:20

20

12

540

1:10

21

8

180

1:10

22

4

540

1:20

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c. EXTRACTION OF THE OIL A flat-bottomed flask of a capacity of 250 mL containing the dried milled sample of known weight was mixed with the selected solvent and placed in the microwave oven for irradiation. This was done with different samples while varying parameters like the power of the microwave over, time taken for irradiation and solute/solvent ratio for 22 runs. The solvent that was selected was n-hexane. The extracts obtained were carefully removed from the microwave and filtered for subsequent analysis while the n-hexane was recovered. For safety purposes, the extraction was done in a fume hood to drastically minimize the exposure of the microwave radiation. The set-up of the experiment is shown below.

Fig. 2: Set-Up of the Microwave-Assisted Extraction

III. RESULTS AND DISCUSSION The Design Expert 10 Software was used for the modelling of design suitable for the extraction process. The Response Surface Model (RSM) was adopted of which the D-Optimal design tool was used. The design generated the expected yield for each solute/solvent ratio as a result of the interaction of each factor on another. The model equations showing the interdependence of each factor on another are presented below: Solute/solvent ratio 1:10 Yield=0.77365 + 6.08886*Time – 0.022458*Power -1.61081E-003* Time* Power – 0.31762* Time2 + 5.04481E005* Power2 Solute/solvent ratio 1:20 Yield=-9.59272 + 6.97360*Time +0.010646*Power -1.61081E-003* Time* Power–0.31762* Time2 + 5.04481E005* Power2 Solute/solvent ratio 1:30

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Yield=19.27040 + 5.85201*Time -0.018354*Power -1.61081E-003* Time* Power – 0.31762* Time2 + 5.04481E005* Power2 In Table 1 and 2 the result of the extraction process showing the relationship between the actual or experimental yield of oil from the Moringa seed and the predicted yield of oil by the software is presented. Table 3 shows the results of test of significance for all regression coefficients. The results showed that the p-values of the model terms were significant, that is p< 0.05. In this research, the three linear terms (A, B, C), one cross-product (BC) and two quadratic terms (A2 and C2) were all remarkably significant model terms at 95% confidence level. However, model terms AB, AC and B2 were less significant than other models. In Table 4, the model F-value of 20.12 implies that the model is significant. There is a 0.01% chance that a large “Model F-value” could occur due to noise. Values of “Prob> F” less than 0.0500 indicate model terms are significant. In this case, A, B, C, BC, A2 are significant model terms. Values greater than 0.1000 indicates that the model terms are not significant. The “Lack of Fit F-value” of 1.79 implies that the Lack of Fit is not significant relative to the pure error. There is a 26.90% chance that a “Lack of Fit F-value” this large could occur due to noise. A non-significant lack of fit is good for the model, as we want the model to fit. R-Squared is a statistical measure of how close the data are to the fitted regression line. In this case, the value of the R-Squared is 0.9568 which indicates that the model fits the data well. The lower value of the Adjusted R-squared (0.9092) as compared to the R-Squared implies that a predictor improved the model by less than expected by chance. It has been suggested that R 2 should be at least 80% for a good fit of a model (Akintunde et al, 2015). The "Pred R-Squared" of 0.7954 is in reasonable agreement with the "Adj R-Squared" of 0.9092; that is, the difference is less than 0.2.

Table 1: Observed oil yield at various runs conducted by varying the parameters Factor A: Time (minus) Factor B: Power (Watts)

Run

Factor C: RESPONSE 1: Solute/Solvent ratio % OIL YIELD (Ratios)

1

12

180

1:10

21.10

2

12

540

1:20

36.45

3

4

180

1:30

34.20

4

4

180

1:10

15.98

5

8

360

1:20

31.90

6

12

180

1:30

38.65

7

4

540

1:30

41.12

8

8

360

1:10

25.42

9

12

540

1:20

40.30

10

12

360

1:10

18.90

11

4

360

1:10

14.12

12

4

540

1:30

39.02

13

8

540

1:30

39.08

14

4

540

1:10

19.60

15

12

540

1:30

40.34

16

4

180

1:30

35.29

17

12

180

1:20

30.30

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18

12

180

1:20

26.40

19

4

540

1:20

28.30

20

12

540

1:10

19.95

21

8

180

1:10

26.20

22

4

540

1:20

31.95

Table 2: Actual Values and Predicted Values by RSM Run

Actual

Predicted

Order

Value

Value

Residual

Leverage

1

21.10

22.22

-1.12

0.582

2

36.45

38.38

-1.92

0.500

3

34.20

34.77

-0.57

0.461

4

15.98

16.48

-0.50

0.651

5

31.90

31.60

0.30

0.548

6

38.65

38.61

0.041

0.846

7

41.12

38.92

2.20

0.420

8

25.43

22.97

2.45

0.389

9

40.30

38.38

1.92

0.500

Table 3: Analysis of variance (ANOVA) of regression equation for Response Surface Quadratic model Source

Sum of Squares

Df

Mean Square

F-Value

P-value

Model

1511.7

11

137.43

20.12

< 0.0001

A (Time)

64.16

1

64.16

9.39

0.0119

B (Power)

58.58

1

58.58

8.58

0.0151

C (Solute/ solvent ratio)

962.42

2

481.21

70.46

< 0.0001

AB

13.14

1

13.14

1.92

0.1956

AC

40.08

2

20.04

2.93

0.0994

BC

70.38

2

35.19

5.15

0.0290

A2

62.21

1

62.21

9.11

0.0129

B2

5.90

1

5.90

0.86

0.3744

Residual

68.30

10

6.83

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Lack of Fit

43.83

5

8.77

Pure Error

24.47

5

4.89

Cor Total

1580.08

21

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0.2690

Not Significant

Table 4: Regression coefficients and significance of response surface quadratic.

Term

Coefficient Estimate

Df

Standard Error

95% CI Low

95% CI High

VIF

Intercept

31.87

1

1.64

28.23

35.52

A-Time

2.57

1

0.72

0.97

4.18

1.37

B-Power

2.41

1

0.70

0.86

3.96

1.26

C[1]

-8.90

1

0.89

-10.88

-6.92

C[2]

-0.27

1

1.02

-2.55

2.00

AB

-1.16

1

0.84

-3.02

0.70

AC[1]

-0.86

1

0.95

-2.98

1.25

AC[2]

2.68

1

1.13

0.15

5.20

BC[1]

-2.23

1

1.01

-4.48

0.013

BC[2]

3.73

1

1.18

1.11

6.35

A2

-5.08

1

1.68

-8.83

-1.33

1.36

B2

1.63

1

1.76

-2.28

5.55

1.48

1.62

Figure 1 shows the response surface plot of the predicted yield of oil against the actual yield of oil both in %w/w.

The plot shows a linear graph that indicates a close proximity between the predicted and the actual value. Figure 2, 3 and 4 show the contour and 3D plots of the effect of time and power on the experimental oil yield at a solute/solvent ratio of 1:10, 1:20 and 1:30 respectively. It was observed that at all the solute/solvent ratios; the yield keeps increasing as power increases. At constant power, the yield for each ratio increases as time increases until the 8min after which the yield starts to decrease as time increases for each solute/solvent ratio. The optimum yield was determined and predicted using the RSM tool of the Design Expert 10 software. Table 2 shows the actual values obtained from the observed experimental results and the values predicted for each run by the RSM. From the table, it was observed that the RSM predicted that the extraction would obtain an optimum yield of 43.60% of the extracted Moringa seed oil (MOSO) when the microwave irradiation power is 540W, and the time of extraction is 8 minutes and the solute/solvent ratio is 1:30. From figure 5 shows the optimal yield as predicted by RSM which was validated by carrying out run 13 again to obtain the yield of the Moringa Seed Oil and compare with the value predicted by RSM. The observed experimental value of the oil yield from the re-run of run 13 was 44.33%. This seems to validate the prediction of RSM because of the proximity of the experimental yield obtained to the predicted oil yield. Thus, the optimum yield was 44.33% when the irradiation power was 540W, irradiation time is 8 minutes and solute/solvent ratio was 1:30.

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50 45

y = 0.9968x R² = 0.955

40 35 30 Predicted Yield

25 20 15 10 5 0 0

10

20

30

40

50

Actual Yield Figure 1: Plot of Predicted Yield by RSM against the Actual Experimental Yield

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Figure 2: Contour and 3D plots showing the effect of Extraction time and Irradiation power on Experimental Moringa Seed oil yield when solute/solvent ratio is 1:10

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Figure 3: Contour and 3D plots showing the effect of Extraction time and Irradiation power on Experimental Moringa Seed oil yield when solute/solvent ratio is 1:20

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Figure 4: Contour and 3D plots showing the effect of Extraction time and Irradiation power on Experimental Moringa Seed oil yield when solute/solvent ratio is 1:30

Figure 5: Predicted Optimal Yield by RSM

III.

CONCLUSION

From the result gotten from the research it can be concluded that the microwave-assisted method of extraction was effective and efficient as the solvent used in this study was minimized and the time of extraction was also considerably shorter than that of the conventional methods of extraction from previous studies. The optimal yield of the process which was 44.33% was gotten at microwave irradiation power of 540W, irradiation time of 8 min and solute/solvent ratio of 1:30. The predicted values obtained from RSM were very close to the actual experimental values as the residual difference was minimal. Thus, the RSM is a good prediction and statistical tool.

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IV. [1] [2] [3] [4] [5]

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