Deployment of response surface methodology to optimize recovery of dark fresh fig (Ficus carica L., var. Azenjar) total phenolic compounds and antioxidant activity

Deployment of response surface methodology to optimize recovery of dark fresh fig (Ficus carica L., var. Azenjar) total phenolic compounds and antioxidant activity

Food Chemistry 162 (2014) 277–282 Contents lists available at ScienceDirect Food Chemistry journal homepage: www.elsevier.com/locate/foodchem Analy...

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Food Chemistry 162 (2014) 277–282

Contents lists available at ScienceDirect

Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Analytical Methods

Deployment of response surface methodology to optimize recovery of dark fresh fig (Ficus carica L., var. Azenjar) total phenolic compounds and antioxidant activity Mostapha Bachir bey ⇑, Leila Meziant, Yassine Benchikh, Hayette Louaileche Laboratoire de Biochimie Appliquée, Faculté des Sciences de la Nature et de la Vie, Université de Bejaia, 06000 Bejaia, Algeria

a r t i c l e

i n f o

Article history: Received 12 January 2014 Received in revised form 27 March 2014 Accepted 13 April 2014 Available online 24 April 2014 Keywords: Fresh dark fig Optimization of extraction Response surface methodology Total phenolic compounds Antioxidant activity

a b s t r a c t Optimum conditions for extracting total phenolic compounds (TPC) and antioxidant activity from fresh dark fig (Ficus carica L.) have been investigated using response surface methodology (RSM). The Box– Behnken design was used to investigate the effects of three independent variables, acetone concentration (40–80%), temperature (25–65 °C), and time (60–120 min), on the response. Regression analysis showed that about 96% of the variation was explained by the models. P-value for the lack of fit was insignificant which confirmed the validity of models. Response surface analysis showed that the optimal extraction parameters that maximized antioxidants extraction were 63.48% acetone, 115.14 min, and 48.66 °C. Under optimum conditions the corresponding experimental values for TPC and antioxidant activity were 536.43 and 71.86 mg GAE/100 g DM. The experimental values are in accordance with those predicted, indicating the suitability of the model and the success of RSM in optimizing the extraction conditions. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Phenolic compounds are ubiquitous secondary metabolites in plants. They comprise a large group of biologically active compounds (above 8000 structures). The research on phenolic extracts has been growing because of the increasing worldwide demand for phenolic compounds and their increasing application in the food industry such as anti-microbial and antioxidant agents and food stabilizers (Brusotti, Ngueyem, Biesuz, & Caccialanza, 2010; Marinova, Ribarova, & Atanassova, 2005). Extraction is the initial and the most important step in the recovery and purification of bioactive compounds from plant materials. Optimization of antioxidants extraction may be achieved by either empirical or statistical methods and is essential for commercial application of the bioactive compounds extraction process (Annegowda, Mordi, Ramanathan, Hamdan, & Mansor, 2012; Rodrigues, Pinto, & Fernandes, 2008). The traditional method of optimization is laborious and time consuming, since one factor at a time is taken into consideration. In this method, the interactions of various factors are ignored and hence, the chances of obtaining the true optimum conditions are dubious (Liyana-Pathirana & Shahidi, 2005). The response ⇑ Corresponding author. Tel.: +213 34 21 47 62. E-mail address: [email protected] (M. Bachir bey). http://dx.doi.org/10.1016/j.foodchem.2014.04.054 0308-8146/Ó 2014 Elsevier Ltd. All rights reserved.

surface methodology (RSM) is a statistical experimental protocol used in mathematical modeling (Gong et al., 2012; Triveni, Shamala, & Rastogi, 2001). This method reduces measurements, improving the statistical interpretation possibility and indicating the interaction between variables (Tsapatsaris & Kotzekidou, 2004; Yim et al., 2012). Fig. are infructescences of the fig tree (Ficus carica L.), a deciduous plant belonging to the Moraceae family. Fig fruit is an important crop consumed worldwide (Solomon et al., 2006). One million tons were produced worldwide; Algeria is the third most important producer of figs with 150,000 tons (FAO, 2011). Figs, particularly dark varieties, are an excellent source of phenolic compounds and present a high antioxidant activity which can prevents several diseases (Solomon et al., 2006; Vinson et al., 1999). Extraction of antioxidant compounds from fig fruits was carried out using various solvent types such as pure or diluted methanol, ethanol, and acetone. Extractions were achieved using different times (5 min–6 h) at room temperature or under slight heating (40–50 °C) (Ercisli et al., 2012; Qusti, Abo-khatwa, & Lahwa, 2010; Solomon et al., 2006; Vallejo, Marín, & Tomás-Barbeán, 2012). Therefore, in the idea to maximize antioxidants extraction from dark fig, we fixed as objective to modelize and optimize the extraction conditions, solvent concentration, temperature, and time, of total phenolics and antioxidant activity using RSM.

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2. Materials and methods

2.5. Measurement of total phenolic compounds

2.1. Standards and reagents

Total phenolic content of extracts was assessed using the Folin– Ciocalteu reagent method (Singleton & Rossi, 1965). Folin–Ciocalteu reagent (750 lL) and sodium carbonate (400 lL, 7.5% w/v) were added to 200 lL of extract. The absorbance at 720 nm was measured in a UV–Vis spectrophotometer (UVmini 1240, Schimadzu, Suzhou Jiangsu, China) after 60 min of incubation. The total phenolic content was expressed as milligrams of gallic acid equivalent (GAE) per 100 g of dry matter (DM).

Folin–Ciocalteu was purchased from Biochem, Chemopharma (Montreal, Quebec), sodium carbonate was from Biochem, Chemopharma (Georgia, USA), gallic acid, acetone, and methanol were from VWR, Prolabo (CE-EMB) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) was procured from Sigma Chemical (Sigma–Aldrich GmbH, Germany).

2.6. Measurement of antioxidant activity

2.2. Sample preparation The fresh dark fig variety, harvested in Beni Maouche locality of Bejaia department (North of Algeria), was used in this study. The dark fig, locally known as Azenjar, presents a dark-purple skin and red pulp with an average weight of 37.34 g. The length and the diameter of the fruit were estimated at 4 cm. The sample (about 1 kg) was randomly harvested and immediately transferred to laboratory where it was mixed, freeze-dried (Alpha1–4 LDplus lyophilizer, Christ, Osterode, Germany), and then was ground (A11 basic grinder, Ika, Staufen, Germany). The obtained powder was stored at 20 °C prior to analysis. 2.3. Selection of appropriate extraction conditions The initial step of the preliminary experiment was to select an appropriate extraction medium for fresh dark fig antioxidants. Effects of solvent nature (acetone, ethanol, methanol, and water), solvent concentration (20–80%), extraction temperature (25– 70 °C), extraction time (0.5–4 h), and sample to solvent ratio (1/ 25–1/100) were tested in our previous study (Bachir bey, Louaileche, & Zemouri, 2013) and the conclusions were used in this study to optimize antioxidants extraction with RSM.

The scavenging capacity for the radical 1,1-diphenyl-2-picrylhydrazyl (DPPH) was used to determine the antioxidant activity according to Molyneux (2004). An aliquot (200 lL) of the extract was added to 1 mL of methanolic DPPH solution (60 lM). The decolorizing process was recorded at 515 nm after 30 min of reaction. The scavenging activity of fig extracts was calculated using a calibration curve achieved with gallic acid and expressed as mg GAE/100 g DM. 2.7. Experimental design One of the common experimental designs used for engineering purposes is a Box–Behnken design that includes three variables and three factorial levels (Radojkovic´ et al., 2012). The independent variables used in this study were acetone concentration (x1, %, v/v), extraction temperature (x2, °C), and time (x3, min) while response variable were TPC and antioxidant activity. Coded and uncoded levels of the independent variables and the experimental design were given in Table 1. Coded value 0 stands for centre point of the variables and was repeated for experimental error. Factorial points were coded as ±1. 2.8. Data analysis

2.4. Extraction procedure An aliquot of lyophilized dark fig (0.67 g) was placed in a 100mL glass vial with 50 mL of solvent containing variable amounts of acetone/water. Extractions were carried out under magnetic stirring at 400 rpm, at different temperature and time (Table 1). The extracts were separated by centrifugation at 5000 rpm (NF 200, Nüve, Turkey) for 10 min.

All experimental data were centered by using three measurements. The response surface regression procedure of JMP 10 (statistical analysis system Inc., SAS) software was used to analyse the experimental data. Experimental data were fitted to a second-order polynomial model and regression coefficients obtained. The generalised second-order polynomial model used in the response surface analysis was as follows equation:

Table 1 Factors and levels for response surface methodology, Box–Behnken design matrix (in coded and uncoded level of three variables), experimental data and predicted values for treelevel-three-factor response surface analysis. Run

Variable levels x1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 a b

80 80 60 40 40 40 60 60 60 80 80 60 60 60 40

(+1) (+1) (0) (1) (1) (1) (0) (0) (0) (+1) (+1) (0) (0) (0) (1)

a

TPC x2

x3

90 (0) 90 (0) 120 (+1) 120 (+1) 90 (0) 60 (1) 90 (0) 60 (1) 60 (1) 60 (1) 120 (+1) 90 (0) 90 (0) 120 (+1) 90 (0)

25 65 25 45 25 45 45 25 65 45 45 45 45 65 65

(1) (+1) (1) (0) (1) (0) (0) (1) (+1) (0) (0) (0) (0) (+1) (+1)

b

Antioxidant activity

b

Observed

Predicted

Observed

Predicted

447.18 475.68 472.92 497.40 389.18 361.10 519.68 392.54 468.94 470.76 527.98 535.08 520.36 485.60 447.70

65.35 70.82 68.71 69.56 62.26 62.92 70.22 65.05 68.18 68.49 69.89 71.28 70.25 69.83 65.62

453.71 482.72 485.24 492.12 382.14 379.94 525.04 380.74 456.62 476.04 509.14 525.04 525.04 497.40 441.18

65.62 69.95 68.48 68.92 63.13 62.96 70.58 64.14 68.41 69.13 69.85 70.58 70.58 70.74 65.35

x1, solvent concentration (%); x2, time (min); x3, temperature (°C). TPC and antioxidant activity were expressed in mg GAE per 100 g DM of fresh dark fig.

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y ¼ a0 þ

3 3 3 X 3 X X X ai xi þ aii x2i þ aij xi xj i¼1

i¼1

ði – jÞ

ð1Þ

i¼1 j¼1

where a0, ai, aii, and aij are the regression coefficients for intercept, linear, quadratic and interaction terms, respectively; xi and xj are the independent variables. Fischer’s test was used for determination of the type of the model equation, while Student’s t-test was performed for the determination of statistical significance of regression coefficients. 2.9. Verification of model Optimal conditions for the extraction of TPC and antioxidant activity from dark fresh fig depended on solvent composition, extraction temperature, and extraction time were obtained using the predictive equations of RSM. The experimental and predicted values were compared in order to determine the validity of the model. 3. Results and discussion 3.1. Analysis of the model The optimization of antioxidants extraction from fresh dark fig was based on maximizing TPC extraction and antioxidant activity. In order to reduce the number of parameters to be tested, several parameters were already tested in a wider range prior to RSM optimization (Bachir bey et al., 2013). Effects of solvent concentration (acetone/water 40–80%, v/v), time (60–120 min), and temperature (25–65 °C) on TPC and antioxidant activity of fig extracts were studied in this investigation. Experiments were performed according to the Box–Behnken design. The mean relative percentage error was 2.106% for TPC and was 6.528% for antioxidant activity, indicating the close agreement between experimental and predicted values. Results also showed that TPC of fresh dark fig ranged from 361.10 to 535.08 mg GAE/100 g DM and antioxidant activity varied between 62.26 and 71.28 mg GAE/100 g DM (Table 1). The regression coefficients of the intercept, linear, quadratic and interaction terms of the model were calculated using the least square technique and were displayed in Table 2. The three linear, two quadratic terms of solvent concentration and temperature parameters were significant at the level of P < 0.05 for both TPC and antioxidant activity. The interaction between solvent and time for antioxidant activity was also significant. The quadratic effects of time and interactions between solvent-temperature and time– temperature for both TPC and antioxidant activity as well as solvent-time for TPC were not significant. The fitted quadratic model for TPC and antioxidant activity (AA) was given in Eqs. (2 and 3), respectively.

TPC ¼ 525:040 þ 28:278x1 þ 36:320x2 þ 22:013x3  37:898x21  47:208x23

ð2Þ

AA ¼ 70:583 þ 1:774x1 þ 1:669x2 þ 1:635x3  1:310x1 x2  2:399x21  2:172x23

ð3Þ

Table 3 presents the results of fitting quadratic model of data. Results of variance analysis (ANOVA) indicate that the contribution of quadratic model was significant for response of the dependent variables, TPC and antioxidant activity. The ANOVA analysis indicates a good model performance with the correlation coefficient (R2) values of 0.957 and 0.958 for TPC and antioxidant activity, respectively. These can explain 96% of calculated model. The statistical analysis gave high significant level, attesting the goodness of

Table 2 Regression coefficient, standard error, and Student’s t-test results of response surface for TPC and antioxidant activity (mg GAE/100 g DM). Parameter

Estimate

Std. error

TPC Intercept x1 x2 x3 x1  x2 x1  x3 x2  x3 x1  x1 x2  x2 x3  x3

t Ratio

Prob > |t|

525.040 28.278 36.320 22.013 19.770 7.505 15.930 37.898 22.833 47.208

10.594 6.488 6.488 6.488 9.175 9.175 9.175 9.549 9.549 9.549

49.560 4.360 5.600 3.390 2.150 0.820 1.740 3.970 2.390 4.940

<0.0001* 0.007* 0.003* 0.019* 0.084 0.451 0.143 0.011* 0.062 0.004*

Antioxidant activity Intercept 70.583 x1 1.774 x2 1.669 x3 1.635 x1  x2 1.310 x1  x3 0.528 x2  x3 0.503 x1  x1 2.399 x2  x2 0.469 x3  x3 2.172

0.577 0.353 0.353 0.353 0.500 0.500 0.500 0.520 0.520 0.520

122.300 5.020 4.720 4.630 2.620 1.060 1.010 4.610 0.900 4.170

<0.0001* 0.004* 0.005* 0.006* 0.047* 0.340 0.361 0.006* 0.409 0.009*

x1, solvent concentration; x2, time; x3, temperature. * P < 0.05.

Table 3 ANOVA table for the effect of acetone concentration, time, and temperature on TPC extraction and antioxidant activity (mg GAE/100 g DM). Source TPC x1 x2 x3 x1  x2 x1  x3 x2  x3 x1  x1 x2  x2 x3  x3 Model Lack of fit Error Total model R2 = 0.957 Adj. R2 = 0.879 Antioxidant activity x1 x2 x3 x1  x2 x1  x3 x2  x3 x1  x1 x2  x2 x3  x3 Model Lack of fit Error Total model R2 = 0.958 Adj. R2 = 0.882

DFa

Sum of squares

F Ratio

Prob > F

1 1 1 1 1 1 1 1 1 9 3 5 14

6396.936 10553.139 3876.401 1563.412 225.300 1015.060 5302.968 1924.885 8228.485 37267.813 1532.0978 1683.531 38951.345

18.999 31.342 11.513 4.643 0.669 3.015 15.750 5.717 24.438 12.298 6.745

0.007* 0.003* 0.019* 0.084 0.451 0.143 0.011* 0.062 0.004* 0.007* 0.139

1 1 1 1 1 1 1 1 1 9 3 5 14

25.170 22.278 21.386 6.864 1.113 1.010 21.253 0.813 17.413 113.816 4.268 4.99614 118.812

25.189 22.295 21.402 6.870 1.114 1.011 21.269 0.813 17.427 12.656 3.907

0.004* 0.005* 0.006* 0.047* 0.340 0.361 0.006* 0.409 0.009* 0.006* 0.210

x1, solvent concentration; x2, time; x3, temperature. * P < 0.05. a Degrees of freedom.

fit of the model in case of the TPC (P = 0.007) and antioxidant activity (P = 0.006). The results indicated that the model could work well for the prediction of the two studied parameters. There was

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no significance in the lack of fit (P > 0.05) in each of the two models (0.139 and 0.210 for TPC and antioxidant activity models, respectively), and it indicated also that the models could be used to predict the responses. The three studied parameters, solvent concentration, time, and temperature, on TPC extraction and antioxidant activity, were found to have positive linear effect. The quadratic effects of the solvent and temperature and the interaction between solvent-time influenced negatively antioxidants extraction. However, interaction terms between solvent concentration-temperature and time–temperature and quadratic effect of time for both TPC and antioxidant activity as well as interaction term between solvent concentration and time for TPC were found to have no effects. 3.2. Analysis of response surfaces The best way of expressing the effect of any independent variable on the TPC extraction and antioxidant activity was to generate surface response plots of models, which were done by varying two variables within the experimental range under investigation and holding the other variable at its central level (0 level). Fig. 1 is the three dimensional plot showing the effects of solvent concentration (x1) and time (x2) on the TPC extraction (Fig. 1a) and antioxidant activity (Fig. 1b) of fresh dark fig. It can be observed that both solvent concentration and time exert significant effects on TPC and antioxidant activity. Examination of the plots in Fig. 1 showed that there were optimal concentration levels for TPC and antioxidant activity. This is due to the quadratic effect of solvent concentration as supported by the results in Table 2. The estimate of the coefficient for the quadratic form of solvent concentration was higher in both TPC and antioxidant activity compared with the quadratic term of time, linear and 2-factor interaction forms. These results are also supported by the ANOVA results in Table 3. Apparently, polarity played an important role in antioxidants extraction. The increase of acetone concentration in the solvent caused a decrease in its polarity, which favored the extraction of less polar components (Cheok, Chin, Yusof, Talib, & Law, 2012). Besides, increase of acetone concentration promoted the breakdown of cell membrane that enhances the permeability of the solvent into the solid matrix (Vatai, Škerget, & Knez, 2009; Zhang, Chen, Wu, & Wang, 2006). Nevertheless, at a very high acetone

concentration the resulting polarity was inappropriate for the extraction of antioxidants from analysed fig. The effects of solvent concentration (x1) and temperature (x3) on TPC extraction and antioxidant activity from fresh dark fig were showed in Fig. 2a and b, respectively. It indicates that solvent concentration and temperature affect highly antioxidants extraction. Examination of the plots in Fig. 2 showed that there are optimum concentration levels for both TPC and antioxidant activity. This is due to the quadratic effect of solvent concentration and temperature on both properties as indicated by the results in Table 2. The estimate of the coefficients for the quadratic and linear form of solvent concentration and temperature were statistically significant in both TPC and antioxidant activity. However, the interaction between the two factors was not found. These results are also supported by the ANOVA results in Table 3. Extraction under higher temperature favored extraction by enhancing both solubility of the solute and diffusion coefficient. Heating softens plant tissues and weakens phenol–protein and phenol–polysaccharide interactions, with more polyphenols diffusion into the solvent (Shi et al., 2003). However, heating cannot increase the phenolic extraction indefinitely. Above 50 °C, the stability of these compounds decreases with dramatic effects on the antioxidant activity (Naczk & Shahidi, 2004). According to Fig. 3, it was observed that both time and temperature influenced TPC extraction and antioxidant capacity, the linear effect of time and quadratic effect of temperature were showed clearly. It was noticed that both factors (time and temperature) operate independently on TPC and antioxidant activity (Tables 2 and 3). The mass transfer from plant material to solvent was related to time and temperature. The mass transfer increase with time until the maximum of extraction was achieved. The temperature accelerate the diffusion, thus increasing the extraction (Kassama, Shi, & Mittal, 2008). For long time of extraction under high temperature, however, the negative quadratic effect became significant. Higher extraction temperature beyond 48.66 °C did not showed significant improvement of TPC extraction and antioxidant activity. This may be attributed to the thermal degradation of antioxidants at high temperature conditions which was favored by long time of extraction (Prommuak, De-Eknamkul, & Shotipruk, 2008), indicating that extracts contained heat sensitive compounds.

Fig. 1. Response surface plots showing the effects of solvent concentration (%) and time (min) on (a) TPC extraction and (b) antioxidant activity (mg GAE/100 g DM) from fresh black fig at the temperature of 45 °C.

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Fig. 2. Response surface plots showing the effects of solvent concentration (%) and temperature (°C) on (a) TPC extraction and (b) antioxidant activity (mg GAE/100 g DM) from fresh black fig at the time of 90 min.

Fig. 3. Response surface plots showing the effects of time (min) and temperature (°C) on (a) TPC extraction and (b) antioxidant activity (mg GAE/100 g DM) from fresh black fig at the solvent concentration of 60%.

The effect of experimental conditions on antioxidant extractions highly differs according to plant material. TPC recovery from Gardenia fruits were linearly enhanced by temperature and time of extraction but water/ethanol ration not significant (Yang, Liu, & Gao, 2009). The extraction of phenolic compounds and antioxidant capacity of maca (Lepidium meyenii) were influence linearly by temperature and ethanol concentration, but time influence only TPC. Quadratic effects of ethanol concentration for TPC and antioxidant activity and temperature for antioxidant capacity were also significant (Campos, Chirinos, Barreto, Noratto, & Pedreschi, 2013).

used for an extraction test for TPC and antioxidant activity. The optimal conditions to obtain the highest extraction of phenolics from fresh dark fig, as well as maximum antioxidant activity, were acetone concentration of 63.48%, temperature of 48.66 °C, and time extraction of 115.14 min. Under optimal conditions, the experimental values for TPC and antioxidant activity were 536.43 ± 5.53 and 68.77 ± 1.43 mg GAE/100 g DM, respectively. These experimental results were in agreement with the predicted values which corresponding to 540.10 mg and 71.86 mg GAE/ 100 g DM.

3.3. Determination and experimental validation of the optimal conditions

4. Conclusion

The optimal conditions were determined by maximizing desirability using JMP prediction profiler. In order to verify the predictive capacity of the model, results of maximized conditions were

High correlation of the mathematical model indicated that a quadratic polynomial model may be employed to optimize the solid–liquid extraction of antioxidants from fresh dark fig From response surface plots, all the three studied factors (acetone

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concentration, temperature, and time) significantly influenced TPC and antioxidant activity of fig extracts. The experimental values were found to be in agreement with the predicted values and clearly indicated the suitability of the developed quadratic models. These results confirm the predictability of the model for the extraction of TPC and antioxidant activity from fresh dark fig in the experimental condition used. Acknowledgements The authors are grateful to the Algerian Ministry of Higher Education and Scientific Research for the financial support and also thank Dr. Farid Dahmoune of Bejaia University (Algeria) for his help in statistical analysis. References Annegowda, H. V., Mordi, M. N., Ramanathan, S., Hamdan, M. R., & Mansor, S. M. (2012). Effect of extraction techniques on phenolic content, antioxidant and antimicrobial activity of Bauhinia purpurea: HPTLC determination of antioxidants. Food Analytical Methods, 5, 226–233. Bachir bey, M., Louaileche, H., & Zemouri, S. (2013). Optimization of phenolic compound recovery and antioxidant activity of light and dark dried fig (Ficus carica L.) varieties. Food Science and Biotechnology, 22, 1613–1619. Brusotti, G., Ngueyem, T. A., Biesuz, R., & Caccialanza, G. (2010). Optimum extraction process of polyphenols from Bridelia grandis stem bark using experimental design. Journal of Separation Science, 33, 1692–1697. Campos, D., Chirinos, R., Barreto, O., Noratto, G., & Pedreschi, R. (2013). Optimized methodology for the simultaneous extraction of glucosinolates, phenolic compounds and antioxidant capacity from maca (Lepidium meyenii). Industrial Crops and Products, 49, 747–754. Cheok, C. Y., Chin, N. L., Yusof, Y. A., Talib, R. A., & Law, C. L. (2012). Optimization of total phenolic content extracted from Garcinia mangostana Linn. hull using response surface methodology versus artificial neural network. Industrial Crops and Products, 40, 247–253. Ercisli, S., Tosun, M., Karlidag, H., Dzubur, A., Hadziabulic, S., & Aliman, Y. (2012). Color and antioxidant characteristics of some fresh fig (Ficus carica L.) genotypes from northeastern turkey. Plant Foods for Human Nutrition, 67, 271–276. FAO. (2011). (Food and Agriculture Organization). Available from: http:// faostat.fao.org Accessed 12.10.13. Gong, Y., Hou, Z., Gao, Y., Xue, Y., Liu, X., & Liu, G. (2012). Optimization of extraction parameters of bioactive components from defatted marigold (Tagetes erecta L.) residue using response surface methodology. Food and Bioproducts Processing, 90, 9–16. Kassama, L. S., Shi, J., & Mittal, G. S. (2008). Optimization of supercritical fluid extraction of lycopene from tomato skin with central composite rotatable design model. Separation and Purification Technology, 60, 278–284. Liyana-Pathirana, C., & Shahidi, F. (2005). Optimization of extraction of phenolic compounds from wheat using response surface methodology. Food Chemistry, 93, 47–56. Marinova, D., Ribarova, F., & Atanassova, M. (2005). Total phenolics and total flavonoids in Bulgarian fruits and vegetables. Journal of the University of Chemical Technology and Metallurgy, 40, 255–260.

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