Response surface optimization of ultrasound assisted extraction of pectin from pomegranate peel

Response surface optimization of ultrasound assisted extraction of pectin from pomegranate peel

G Model ARTICLE IN PRESS BIOMAC 4685 1–6 International Journal of Biological Macromolecules xxx (2014) xxx–xxx Contents lists available at Science...

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ARTICLE IN PRESS

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International Journal of Biological Macromolecules xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

International Journal of Biological Macromolecules journal homepage: www.elsevier.com/locate/ijbiomac

Response surface optimization of ultrasound assisted extraction of pectin from pomegranate peel

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I. Ganesh Moorthy a , J. Prakash Maran b,∗ , S. Muneeswari @ Surya a , S. Naganyashree a , C.S. Shivamathi a a b

Department of Biotechnology, Kamaraj College of Engineering and Technology, Virudhunagar 626 001, Tamil Nadu, India Department of Food Technology, Kongu Engineering College, Perundurai 638 052, Erode, Tamil Nadu, India

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a r t i c l e

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a b s t r a c t

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Article history: Received 2 September 2014 Received in revised form 17 October 2014 Accepted 19 October 2014 Available online xxx

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Keywords: Extraction Pectin Pomegranate peel

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1. Introduction

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Ultrasound assisted extraction of pectin from waste pomegranate peel was investigated and optimized using Box–Behnken response surface design coupled with numerical optimization technique. The individual and interactive effect of process variables (solid-liquid ratio, pH, extraction time and temperature) on the pectin yield was studied. The experimental data obtained were analyzed by Pareto analysis of variance (ANOVA) and second-order polynomial models were developed using multiple regression analysis. The models developed from the experimental design were predictive and good fit with the experimental data with high coefficient of determination (R2 ) value. The optimal extraction condition was found to be 1:17.52 g/ml of solid-liquid ratio, 1.27 of pH, 28.31 min of extraction time and 61.90 ◦ C of extraction temperature respectively. Under the optimal conditions, experimental yield was very close to the predicted values. © 2014 Published by Elsevier B.V.

Food industry generates a high volume of processing byproducts and wastes, which in several instances might pose severe environmental problems. The inadequate disposal of these wastes can cause pollution problems as well as a loss of a valuable material for other processes [1]. The food wastes/by-products can be a major pool of substances with very high potential to the pharmaceutical, food and cosmetics manufacturing, while the development of processes for their (bio)-production and recovery would provide an irrefutable economic benefit for the agri-food sector and a direct, positive environmental impact [2]. Pomegranate (Punica granatum) is one of the fruits containing important bioactive phenolic ingredients belonging to the Punicaceae family and has been widely used as botanical ingredients in herbal medicines and dietary supplements [3]. Pomegranate is increasingly consumed as various processed products, such as juices, wines, jams, jellies and extracts. In pomegranate juice processing, 1 ton of fresh fruit generates 669 kg by-product pomegranate marc containing 78% peel and 22% seeds [4]. Pomegranate peels are one of the most valuable by-products

∗ Corresponding author. Tel.: +91 4294 226606; fax: +91 4294 220087. E-mail address: [email protected] (J.P. Maran).

of the food industry. In the past several years, many reports focus on the extraction, chemical structure and biological activities of the antioxidants extracted from the Pomegranate peels [5,6]. Whereas, little attention was devoted to the extraction of the pectin from Pomegranate peels. Among the various non-conventional extraction methods, ultrasound-assisted extraction (UAE) has shown high extraction efficiency, low energy and solvent consumptions and thereby its usage as an alternative method has been on the rise [7]. The mechanism of ultrasound-assisted extraction is attributed to mechanical, cavitation, and thermal efficacies which can result in disruption of cell walls, particle size reduction, and enhanced mass transfer across cell membranes [8]. Response surface methodology (RSM) is one of the multivariate techniques that can deal with experimental design and statistical modeling. It is used to examine the relationship between one or more response variables and a set of quantitative experimental variables or factors [9]. Box–Behnken response surface design (BBD) is a spherical, revolving response surface methodology (RSM) design that consists of a central point and the middle points of the edges of the cube circumscribed on the sphere. It consists of three interlocking 22 factorial designs with points lying on the surface of a sphere surrounding the center of the design. This design has been applied for several chemical and physical processes to study and optimize the effect of process variables [10].

http://dx.doi.org/10.1016/j.ijbiomac.2014.10.037 0141-8130/© 2014 Published by Elsevier B.V.

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From the extensive literature analysis, it was found that, no research report has been found on the ultrasound-assisted extraction of pectin from waste pomegranate peel. Therefore, the present study has been planned to investigate and optimize the process parameters (solid-liquid ratio, pH, extraction time and temperature) on the maximum recovery of pectin from waste pomegranate peel by UAE method using four factors three levels BBD.

Experimental data were fitted to a second-order polynomial mathematical equation in order to express the relationship between independent variables and responses. The generalized form of second order polynomial equation was given as follows Y = ˇ0 +

k  j=1

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2. Materials and methods

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2.1. Materials

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Waste pomegranate peel was obtained from the local fruit processing unit near Virudhunagar, Tamilnadu, India. The waste peel was dried in a hot air oven (50 ◦ C) until it attains constant weight. The dried residuals were crushed into powder using a grinder, stored in sealed plastic bags prior to experiments. All the chemicals used in the study were analytical grade and purchased from Merck chemicals, Mumbai. 2.2. UAE of pectin An ultrasonic device (VCX 130, Sonics Vibra Cell, USA and 0–130 w) with 2.00 cm flat tip probe was used to perform the experiments and it has the provisions to set required output power, temperature and time. The ultrasonic generator probe was directly submerged into the suspension (10 g of dried power with appropriate volume of distilled water) and continuous ultrasonic waves were passed at a frequency of 20 kHz. The experiments were carried out in triplicates according to the Table 1 and the average results were reported. During the extraction period, an amplitude controller was used to set and control the desired level of temperature within ±1 ◦ C. After extraction for selected time, the suspension was filtered using filter paper (Whatman no-1), centrifuged (5500 rpm for 15 min) and the supernatant was precipitated with an equal volume of 95% (v/v) ethanol. The coagulated pectin mass was washed with 95% (v/v) ethanol for three times in order to remove the mono and disaccharides. After extraction, the wet pectin was subjected to drying at 50 ◦ C in the hot air oven until it attains a constant weight.

ˇj Xj +

k 

ˇjj X2j +

j=1

k  i

ˇij Xi Xj

where, Y is the response; Xi and Xj are variables (i and j range from 1 to k); ˇ0 is the model intercept coefficient; ˇj , ˇjj and ˇij are interaction coefficients of linear, quadratic and the second-order terms, respectively; k is the number of independent parameters (k = 4 in this study) [13]. Statistical analysis of the experimental data was performed using the Stat ease Design Expert 8.0.7.1 statistical software (Stat-Ease Inc., Minneapolis, USA). 2.4. Optimization and validation of optimized condition

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PY (%) =

m  0

m

× 100

(1)

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where m0 is the weight of dried pectin (g) and m is the weight of dried pomegranate waste peel powder (g).

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2.3. Experimental design

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In this study, four factors, three levels Box–Behnken response surface design (BBD) was employed to investigate and optimize the effect of process variables such as SL ratio (1:10–1:20 g/ml), pH (1–2), extraction time (15–35 min) and extraction temperature (50–70 ◦ C) on the maximum extraction yield of pectin from waste pomegranate peel. A total number of 29 experiments with five replicates (centre point, used to estimate experimental error) were ascertained and total numbers of experiments (N) were calculated according to the method described by Mara et al. [12].

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Numerical optimization technique was adapted in this study to optimize the process conditions. For optimization of process variables, the regression model developed in this study was used to determine the optimal condition which could provide maximum pectin yield. The nature of the optimal condition (point of maximum or minimum or a saddle point response) was also evaluated by transforming the developed regression model into conical form and the Eigen values were computed using MATLAB software. To determine the validity of optimized condition, additional triplicate experiments were performed under optimal conditions and average values of the experiments were compared with the predicted values of the optimized conditions in order to find out the accuracy and suitability of the optimized conditions.

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3. Results and discussion

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3.1. Model fitting and statistical analysis

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By applying multiple regression analysis on the experimental data, a second-order polynomial equation includes linear, interactive and quadratic terms was developed which can express the relationship between process variables and the responses. The final equation obtained in terms of coded factors is given below:

3.3X2 X3 − 1.19X2 X4 + 1.42X3 X4 − 4.97X21 − 8.12X22 − 4.98X23 − 5.05X24 The pectin yield (PY) was calculated (dry basis) from the following equation [11].

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PY(%) = 24.05 + 4.06X1 − 1.29X2 + 3.94X3 − 0.31X4 − 1.43X1 X2 − 1.52X1 X3 − 0.23X1 X4 +

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(2)

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(3)

The statistical significance of the developed model was evaluated using analysis of variance (ANOVA). ANOVA is a statistical technique that subdivides the total variation in a set of data into component parts associated with specific sources of variation for the purpose of testing hypotheses on the parameters of the model [14]. The regression coefficients and p-value for the second-order polynomial equation is presented in Table 2 and it indicated that the equation adequately represented the actual relationship between the response and their significant variables. The ANOVA result showed that, the higher model F-value (83.18) associated lower p-values (p < 0.0001) indicate that most of the variation in the response can be explained by the regression models. Determination of co-efficient (R2 ), Adjusted- R2 , predicted- R2 and coefficient of variation (CV %) was also calculated to check the adequacy of the model [15,16]. The high R2 (0.988), adj-R2 (0.976) and pre-R2 (0.932) values clearly demonstrated that, the developed model is precise in exhibiting the relationship between the response and independent variables. Low values of coefficient of variance (6.69) displayed the high degree of precision and good reliability of the conducted experiments [17]. In this study, the adequate precision

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Table 1 Box–Behnken experimental design matrix with observed and predicted values. Factors

Unit

Symbols

Level of factors −1

SL ratio pH Extraction time Extraction temperature Std order

g/ml min ◦ C X1

X1 X2 X3 X4 X2

X3

1:10 1 12 50 X4

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

−1 1 −1 1 0 0 0 0 −1 1 −1 1 0 0 0 0 −1 1 −1 1 0 0 0 0 0 0 0 0 0

−1 −1 1 1 0 0 0 0 0 0 0 0 −1 1 −1 1 0 0 0 0 −1 1 −1 1 0 0 0 0 0

0 0 0 0 −1 1 −1 1 0 0 0 0 −1 −1 1 1 −1 −1 1 1 0 0 0 0 0 0 0 0 0

0 0 0 0 −1 −1 1 1 −1 −1 1 1 0 0 0 0 0 0 0 0 −1 −1 1 1 0 0 0 0 0

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(signal to noise ratio) was found to be > 31 for response, which indicates the best fit of developed model.

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3.2. Analysis of model competence

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The model competence was investigated by various influential plots ((normal % probability, predicted versus actual, predicted values versus internally studentized residual and Cook’s distance plot) and the results were shown in Fig. 1. From the results, it was observed that, the data’s were stretched out on the diagonal line

0

1

1:15 1.5 25 60 Pectin yield (%) Experimental 7.66 17.31 7.09 11.01 12.05 16.21 8.62 18.44 9.32 17.70 9.31 18.63 11.71 1.89 12.81 16.20 4.23 15.97 16.20 21.87 11.58 12.87 12.22 8.76 23.98 24.18 24.09 23.96 24.17

1:20 2 35 70 Predicted 6.76 17.73 7.04 12.29 11.81 16.85 8.35 19.06 10.52 18.16 9.42 18.00 11.60 2.41 12.86 16.89 4.58 15.72 15.49 20.57 11.30 11.09 13.04 8.09 24.05 24.05 24.05 24.05 24.05

(Fig. 1a and b) and indicted a good correlation between experimental and predicted data. The residual values were low and appeared to be scattered randomly within the range. Hence trends observed in the Fig. 1 showed that, the developed model is precise and has the ability to predict the experimental data. 3.3. Influence of process variables The influence of process variables (individual and interactive effect) over the extraction yield of pectin was investigated with the

Source

Coefficient estimate

Sum of squares

Degree of freedom

Standard error

Mean square

F value

p-value

Model X1 X2 X3 X4 X12 X13 X14 X23 X24 X34 X1 2 X2 2 X3 2 X4 2 Residual Std. Dev. Mean C.V. % PRESS

24.05 4.06 −1.29 3.94 −0.31 −1.43 −1.52 0.23 3.30 −1.19 1.42 −4.97 −8.12 −4.98 −5.05

1090.90 197.39 19.95 186.06 1.18 8.20 9.19 0.22 43.67 5.62 8.02 160.27 427.68 161.10 165.14 13.12

14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14

0.43 0.28 0.28 0.28 0.28 0.48 0.48 0.48 0.48 0.48 0.48 0.38 0.38 0.38 0.38

77.92 197.39 19.95 186.06 1.18 8.20 9.19 0.22 43.67 5.62 8.02 160.27 427.68 161.10 165.14 0.94

83.18 210.70 21.30 198.61 1.26 8.75 9.81 0.23 46.62 6.00 8.56 171.08 456.53 171.96 176.28

<0.0001 <0.0001 0.0004 <0.0001 0.2803 0.0104 0.0073 0.6358 <0.0001 0.0280 0.0111 <0.0001 <0.0001 <0.0001 <0.0001

R2 Adj-R2 Pred-R2 Adequate Precision

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Table 2 ANOVA for second order regression model.

0.97 14.48 6.69 75.54

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0.988 0.977 0.932 31.08

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Fig. 1. Diagnostic plots for model adequacy.

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help of three dimensional (3D) response surface plots. The 3D plots were generated by maintaining two factors at their constant level (in turn its central level), whereas the other two factors were varied in their range [18] and the results are depicted in Fig. 2. The extraction yield is greatly influenced by SL ratio. Thus, SL ratios influence over the extraction yield was studied by measuring it at different SL ratios ranging from 1:10 to 1:20 g/ml (Fig. 2). On increasing the SL ratio up to 1:15 g/ml the concentration and viscosity of the extraction solvent decreased, this lead to higher dissolution of pectin in the solvent and augmented the yield. Cavitation effects are produced as a result of the collasing of cavities which are produced as a result of the passage of ultrasound waves in the solvent. During the cavitation effect a large amount of energy and temperature will be released by the collapsing cavities [19]. This in

turn leads to the disruption of the cell walls or the enlargement of the pore in walls, which enhances penetration rate of solvent into plant matrix, thus results in increased pectin yield. But as the SL ratio increases the viscosity of the medium increases which leads to the reduction in the intensity of cavitation, because the negative pressure in the rarefaction region of wave function should overcome the natural cohesive forces for the formation of cavitation [20]. Therefore, on further increment of SL ratio (1:15 g/ml,) lead to the decrement in extraction yield. The results clearly demonstrate that on decreasing pH values the PY was found to increasing (Fig. 2). The acidic extraction solvent hydrolysis of the insoluble pectin constituents into soluble pectin as it comes in contact with it, thus the pectin recovery is maximum at acidic pH(1.6) [21]. However, on increasing the pH value beyond

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Fig. 2. Effect of process variable on pectin yield.

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1.6, the PY was decreased. This might be due to the aggregation of pectin which retarded the pectin release. Fig. 2 demonstrates the effect of time on the extraction of pectin. The yield of pigment was found to increase on increasing the time up to 30 min and then it was found to decrease on further increase in time. This is because, the cavitation effect of the ultrasonic waves accelerated the swelling and hydration of plant material could be by during the earlier period of extraction [22]. The penetration of solvent [8] into the plant matrix and the disruption of cell wall were enhanced by the collapse of micro-bubbles near surfaces and micro-jets formation [23] thus in turn it enhances the extraction performance. Fick’s second law of diffusion, which stated that the final equilibrium between the solute concentrations in the solid matrix (plant matrix) and in the bulk solution (solvent) could be achieved after certain time, can explain this. However, an excessive exposure of ultrasound treatment can causes the structural decomposition and destruction of pectin, which decreases the yield of extraction. Effects of temperature over the extraction yield of pigments were studied and the results are given in (Fig. 2). On the

increasing temperature from 50 to 65 ◦ C, the yield of pigments was found to be increased and it decreased on further increment. The solubility and diffusivity of solid from the plant materials was found to increase with the increase in temperature [24,25]. The surface tension and viscosity of the solvent was reduced while increasing the temperature, which changes characteristics of ultrasonic cavitation and intensity of mass transfer enhancement and degradation of pectin occurs [26]. Thus results in the lower pectin yield on further increment of temperature. 3.4. Determination of optimal extraction In order to extract maximum yield of pectin from waste pomegranate peel a regression model (Eq. (4)) developed to find out the optimal extraction condition. With respect to X1 , X2 , X3 and X4 respectively the first and second order derivatives of were derived. As all the second order derivatives showed negative values, it implies the applicability of maximization [27]. Thus, to get the maximum extraction yield of pectin, second order derivatives were equated with zero and solved for X1 , X2 , X3 and X4 . Algebraic

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Fig. 3. Desirability ramp for optimized condition.

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solution in the coded form of optimal condition was found to be: X1 = 0.504 g/ml, X2 = −0.454, X3 = 0.331 min and X4 = 0.190 ◦ C. The corresponding experimental parametric values were: 1:17.52 g/ml (SL ratio), 1.27 (pH), 28.31 min (extraction time) and 61.90 ◦ C (extraction temperature) respectively. The predicted pectin yield was 24.05% (Fig. 3) at the optimal conditions. The regression equation was transformed to the canonical form for determining the nature of the optimal point, and MATLAB 7.1 software was used for finding the Eigen values. The Eigen values obtained were negative (X1 = −9.04, X2 = −5.70, X3 = −4.91 and X4 = −3.47) which implies that, the determined optimal condition was a maximum point.

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3.5. Validation of optimized condition

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However, on considering the actual production, the optimal conditions can be modified as follows: SL ratio of 1:18 g/ml, pH of 1.3, extraction time of 29 min and extraction temperature of 62 ◦ C respectively. The additional experiments (triplicates) were carried at these optimal conditions in order to compare the predicted results with experimental value. Under these conditions, the experimental yield of pectin (23.87 ± 0.28%) was close to with predicted value (23.92%). Thus, for predicting the maximum extraction yield of using ultrasound assisted extraction technique Box–Behnken design was considered an accurate tool.

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4. Conclusion

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In this study, with a four factors three level Box–Behnken response surface design the extraction conditions for extraction of pectin from waste pomegranate peel by UAE process were optimized. A high correlated quadratic polynomial mathematical model was developed and its significance was investigated. The optimal conditions were: 1:17.52 g/ml of SL ratio, 1.27 of pH, 28.31 min of extraction time and 61.90 ◦ C of extraction temperature respectively. Under these optimal conditions, experimental yield was very close to the predicted values.

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