Response surface optimization of ultrasound-assisted polysaccharides extraction from pomegranate peel

Response surface optimization of ultrasound-assisted polysaccharides extraction from pomegranate peel

Food Chemistry 177 (2015) 139–146 Contents lists available at ScienceDirect Food Chemistry journal homepage: www.elsevier.com/locate/foodchem Analy...

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Food Chemistry 177 (2015) 139–146

Contents lists available at ScienceDirect

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

Analytical Methods

Response surface optimization of ultrasound-assisted polysaccharides extraction from pomegranate peel Cai-ping Zhu ⇑, Xi-chuan Zhai, Lin-qiang Li, Xiao-xia Wu, Bing Li College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi’an 710062, China

a r t i c l e

i n f o

Article history: Received 10 March 2014 Received in revised form 30 August 2014 Accepted 3 January 2015 Available online 8 January 2015 Keywords: Pomegranate peel Polysaccharide Ultrasound-assisted extraction Response surface methodology

a b s t r a c t Ultrasonic technique was employed to extract polysaccharides from pomegranate peel. The optimal conditions for ultrasonic extraction of pomegranate peel polysaccharide (PPP) were determined by response surface methodology. Box–Behnken design was applied to evaluate the effects of four independent variables (ratio of water to raw material, extraction time, extraction temperature, ultrasonic power) on the yield of PPP. The correlation analysis of mathematical-regression models indicated that quadratic polynomial model could be employed to optimize the ultrasonic extraction of PPP. The optimum extraction parameters were as follows: ratio of water to raw material, 24 ml/g; extraction time, 63 min; extraction temperature, 55 °C; and ultrasonic power, 148 W. Under these conditions, the polysaccharide yield was 13.658 ± 0.133% for the pomegranate peel, which well matches with the predicted value. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Owing to many researchers recognized the importance of traditional medicine as an affordable source of health care, the extraction and purification of bioactive compounds from natural sources have become more and more important for the utilization of phytochemicals in the preparation of functional food ingredients, pharmaceutical and cosmetic products. Pomegranate is an important source of bioactive compounds and has been used for folk medicine for many centuries. Many researches have shown that pomegranate juice has nutritional and medical benefits such as antioxidative, anticancer, and antimutagenic efficacy (Adams et al., 2006; Adhami & Mukhtar, 2006; Bolling, Chen, & Chen, 2013; Faria, Monteiro, Mateus, Azevedo, & Calhau, 2007; Heber et al., 2007; Tezcan, GültekinÖzgüven, Diken, Özçelik, & Erim, 2009; Yasoubi, Barzegar, Sahari, & Azizi, 2007). Because of these beneficial health effects, now some researchers begin to investigate the physicochemical and sensory characterization and volatile profiles of pomegranate extracts and liquors (Galego, Jockusch, & Silva, 2013; Vázquez-Araújo et al., 2014). The outputs of pomegranate and pomegranate juice in the world have increased rapidly in recent years. In China, with the development of juice processing, about thousands of tons of by-products generate each year. The by-products are normally called pomegranate marc, only a few of them used as the Chinese ⇑ Corresponding author. Tel./fax: +86 29 85310517. E-mail address: [email protected] (C.-p. Zhu). http://dx.doi.org/10.1016/j.foodchem.2015.01.022 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved.

medicine, all the others are either used as cattle feed or directly disposed as waste. Some measurements founded that pomegranate marc contains 78% peel and 22% seeds on a wet basis (w.b.) (Qu et al., 2009). The peels had higher content of antioxidants than the seeds and could be a good source for producing high-value antioxidants (Pan, Qu, Ma, Atungulu, & McHugh, 2012; Qu et al., 2009). In addition, some scientific publications have shown that the pomegranate peel extract have anti-inflammatory, removing heavy metal, antioxidative, anti-microbial, anti-infective, antimutagenic, hepatoprotective properties (Bachoual, Talmoudi, Boussetta, Braut, & El-Benna, 2011; El-Ashtoukhy, Amin, & Abdelwahab, 2008; Hayrapetyan, Hazeleger, & Beumer, 2012; Ismail, Sestili, & Akhtar, 2012; Li et al., 2006; Negi, Jayaprakasha, & Jena, 2003; Shaban, El-Kersh, El-Rashidy, & Habashy, 2013; Ventura et al., 2013), so pomegranate peels are the most valuable by-products of the food industry. Pomegranate peels are rich in polyphenols and polysaccharides (Lansky & Newman, 2007). Almost all the researches are focused on the polyphenols extracted from the pomegranate peels, while a little attention was devoted to the polysaccharides. In recent decades, plant polysaccharides have attracted a great deal of attention in the biomedical field due to their broad spectra of therapeutic properties and relatively low toxicity (Schepetkin & Quinn, 2006). A variety of bioactivities, such as antioxidation, immunomodulation, anticancer and hypoglycemic activity, have been confirmed for polysaccharides (Xie et al., 2010, 2013; Zhang, Nie, Huang, Li, & Xie, 2013; Zhu et al., 2013). The extraction of pomegranate peel polysaccharides

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Table 1 Box–Behnken experimental design and results for yield of PPP. Run number

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Coded levels

Yield of PPP (%)

X1 Ratio of water to raw material (ml/g)

X2 Extraction time (min)

X3 Extraction temperature (°C)

X4 Ultrasonic power (W)

+1(25) +1(25) 1(15) 0(20) 1(15) 0(20) 0(20) 0(20) 0(20) 0(20) 1(15) 0(20) 0(20) 0(20) 0(20) 0(20) 0(20) 1(15) +1(25) +1(25) 0(20) 0(20) 1(15) 0(20) 0(20) 0(20) +1(25) +1(25) 1(15) 0

+1(70) 1(50) +1(70) 0(60) 1(50) 0(60) 0(60) 0(60) 0(60) 0(60) 0(60) +1(70) 0(60) 1(50) +1(70) 0(60) 1(50) 0(60) 0(60) 0(60) +1(70) +1(70) 0(60) 0(60) 1(50) 0(60) 0(60) 0(60) 0(60) 1(50)

0(60) 0(60) 0(60) +1(70) 0(60) 0(60) 0(60) 1(50) 1(50) +1(70) 0(60) 1(50) 0(60) +1(70) +1(70) 0(60) 1(50) 0(60) 0(60) 0(60) 0(60) 0(60) +1(70) 0(60) 0(60) 0(60) 1(50) +1(70) 1(50) 0(60)

0(140) 0(140) 0(140) +1(160) 0(140) 0(140) 0(140) 1(120) +1(160) 1(120) +1(160) 0(140) 0(140) 0(140) 0(140) 0(140) 0(140) 1(120) 1(120) +1(160) 1(120) +1(160) 0(140) 0(140) 1(120) 0(140) 0(140) 0(140) 0(140) +1(160)

(PPP) by a conventional solvent extraction (CSE) method has been investigated in a previous work (Zhu & Liu, 2013). Although CSE is simple and safe, it needs high temperature and long extraction time. Ultrasound-assisted extraction (UAE) has been used in many phytochemicals extraction (Le & Le, 2012; Porto, Decorti, & Natolino, 2013), which is an advanced method proposed recently. The acoustic cavitation in UAE causes disruption of the cell walls, reduction of the particle size and enhancement on contact between solvents and targeted compounds (Rostagno, Palma, & Barroso, 2003). As UAE has lower energy consumption, lower consumption of solvents, higher extraction efficiency and higher level of automation, UAE is preferable to CSE. Thus, the UAE method was used in this study for further improving the PPP extraction performance. In this paper, the ultrasonic extraction parameters such as extraction time, extraction temperature, solvent/sample ratio, and ultrasonic power were optimized with the response surface methodology (RSM) employing a four-variable, three-level Box– Behnken design (BBD), for the polysaccharides extraction from the pomegranate peel powder.

2. Material and methods 2.1. Materials Pomegranate peels were obtained from the fresh fruit purchased from the local market in Lintong City, Shaanxi Province, China. The peels were separated manually from the fruit, sun-dried and powdered, and then kept at room temperature for further study. All the chemicals and solvents used were of analytical grade and obtained from Xi’an, Shaan xi province, China.

13.05 12.89 12.37 12.42 11.68 13.76 13.70 12.21 12.52 12.54 11.29 13.36 13.61 12.69 12.32 13.65 11.82 12.27 12.09 12.98 12.63 12.78 12.43 13.51 12.23 13.63 13.29 12.81 12.07 12.32

2.2. Ultrasound-assisted extraction The ultrasound-assisted extraction was performed in an ultrasonic cleaning bath (KQ-200KDE type, 40.0 kHz, Kunshan ultrasonic instrument Co. Ltd., Jiangsu, China) with a usable capacity of 2.5 L (the internal dimensions: 30.0  15.0  15.0 cm). An inwater pipe was added to the opposite of out-water pipe in the bath, and the flux ratio between in-water and out-water was regulated to keep solution temperature stable in the test. Dried ground Pomegranate peel samples (5.0 g) were extracted with distilled water (ratio of water to raw material (ml/g) ranging from 10:1 to 30:1) at pH 6.5–7.5 (adjusting the suspension pH by 0.1 mol/L NaOH or HCl), while the temperature of the water bath was kept steady for a given temperature (within ±1.0 °C, extraction temperature ranging from 40 to 80 °C). The water-material slurry was placed into a 250 ml conical flask in the ultrasonic cleaning bath with a given ultrasonic power (ultrasonic power ranging from 100 to 200 W) for a given time (extraction time ranging from 20 to 70 min) during the entire extraction process. The extracted slurry was centrifuged at 2000g for 10 min to collect the supernatant, and the insoluble residue was treated again as mentioned above. The supernatant was incorporated and concentrated to onefifth of initial volume using a rotary evaporator (RE-52AA, Yarong Technology and Science Inc., Shanghai, China) at 55 °C under vacuum. The resulting solution was mixed with four volumes of dehydrated ethanol (ethanol final concentration, 80%) and kept overnight at 4 °C. Then the solution was centrifuged at 2000g for 10 min, washed three times with dehydrated ethanol, and the precipitate was collected as PPP. The extract was air-dried at 50 °C until its weight was constant, and then was weighted with a balance (JA2003N, Tole Metrical Scientific and Technical Co., Shanghai, China). The percentage PPP yield (%) is calculated as follows:

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yield of PPP (%)

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5

yield of PPP (%)

ingredients on the yield of PPP. After determining the significant factors, the optimum operation conditions are attained by using more complex experimental designs such as Doehlert matrix (DM), central composite designs (CCD) and three-level designs such as the Box–Behnken design (BBD) (Cai, Gu, & Tang, 2008; Chopra et al., 2007; Zhang et al., 2012). A four-factor, three-level design used is suitable for exploring quadratic response surfaces and constructing second-order polynomial models with DesignExpert 8.0.5 Trial (State-Ease, Inc., Minneapolis, MN, USA). The Box–Behnken design was specifically selected since it requires fewer runs than a CCD in cases of three or four variables. This cubic design is characterized by set of points lying at the midpoint of each edge of a multidimensional cube and center point replicates (n = 3) whereas the ‘missing corners’ help the experimenter to avoid the combined factor extremes. This property prevents a potential loss of data in those cases (Box & Behnken, 1960). For statistical calculations, the relation between the coded values and actual values are described as the following equation:

a

11.00 10.50 10.00 9.50 9.00 8.50 8.00 7.50 7.00

10 15 20 25 30 ratio of water to raw material (ml/g)

13.00 12.50 12.00 11.50 11.00 10.50 10.00 9.50 9.00 8.50 8.00

35

b

X i ¼ ðAi  A0 Þ=DA;

10

20

30

40

50

60

70

80

yield of PPP (%)

extraction time (min)

c

14.00 13.50 13.00 12.50 12.00 11.50 11.00 10.50 10.00 9.50 9.00

4 4 4 X 4 X X X bi X i þ bii X 2i þ bij X i X j ; i¼0

40

50

60

70

80

90

extraction temperature (ć)

yield of PPP (%)

where Xi is the (dimensionless) coded value of the variable Xi, Ai is the actual value of variable; A0 is the actual value of Ai at the center point, and DA is the step change. A design matrix comprising of 27 experimental runs was constructed. The behavior of the system was explained by the following quadratic equation:

Y ¼ b0 þ

30 14.00 13.50 13.00 12.50 12.00 11.50 11.00 10.50 10.00 9.50 9.00

d

ð1Þ

j¼0

ð2Þ

i¼0 j¼0

where Y is the dependent variable, b0 is constant, and bi is regression coefficients computed from the observed experimental values of Y; and Xi is the coded levels of independent variables. The terms XiXj and Xi2 represent the interaction and quadratic terms, respectively. The dependent and independent variables selected are shown in Table 1 along with their low, medium, and high levels, which were selected based on the results from preliminary experimentation. The concentration range of ratio of water to raw material (X1), extraction time (X2), extraction temperature (X3), and ultrasonic power (X4) used to prepare the 30 formulations and the respective observed responses are given in Table 1. 3. Results and discussion 3.1. Effects of operation parameters of UAE on the yield

80

100

120

140

160

180

200

220

ultrasonic power (W) Fig. 1. The effect of ratio of water to raw material (a), extraction time (b), extraction temperature (c) and ultrasonic power (d) on the yield of PPP (n = 3).

3.1.1. Effects of ratio of water to raw material on the yield of PPP The extraction efficiency of UAE was influenced by various factors. Fig. 1a listed the effect of ratio of water to raw material on the yield of polysaccharides and other extraction conditions were fixed as follows: extraction time 30 min, extraction temperature 50 °C, and ultrasonic power 140 W. As shown in Fig. 1a, the yield of PPP increased rapidly when the ratio of water to raw material ranged from 10:1 to 20:1 ml/g, but there was no obvious

PPP yield ð%Þ ¼ m0 =m  100 m0 (g) is the dried PPP weight; m (g) is the dried raw material weight. 2.3. Experimental design and statistical analysis Box–Behnken statistical screening design was used to statistically optimize the formulation parameters and evaluate main effects, interaction effects and quadratic effects of the formulation

Table 2 Analysis of variance for the fitted quadratic polynomial model of extraction of PPP. Source

SS

DF

MS

F-Value

Prob > F

Model Residual Lack of fit Pure error Cor total

11.94 0.17 0.16 9.8  103 12.24 R2 = 0.9863

14 13 10 3 29 R2adj = 0.9716

0.85 0.013 0.016 3.267  103

66.96

<0.0001

4.77

0.1126

CV = 0.89

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change in the yield of PPP as the ratio continued to increase. A larger ratio of water to raw material implied greater concentration difference between the interior plant cells and the exterior solvent, and the diffusion of polysaccharides occurred more quickly. However, a high ratio of water to raw material prolonged the distance of diffusion toward the interior tissues. Therefore, the yield increased slowly when the ratio of water to raw material increased from 20:1 to 30:1 ml/g. To avoid the wasting consumption of solvents and bulky handling in the subsequent processes, 20:1 ml/g was chosen as the optimum ratio of water to raw material for commercial application. 3.1.2. Effects of extraction time on the yield of PPP Extraction time is another factor that would influence the extraction efficiency and selectivity of the fluid. This might be due to the time requirement of the exposure of the PPP to the release medium where the liquid penetrated into the dried powdered material, dissolved the PPP and subsequently diffused out from the material (Ye & Jiang, 2011). The effect of extraction time on the yield of PPP was studied with the extraction temperature 50 °C, ultrasonic power 140 W and the ratio of water to raw material 20:1 mg/g. The extraction time was set at 20, 30, 40, 50, 60 and 70 min. It could be found that the extraction yield increased as extraction time ascended from 20 to 50 min, and then increased slowly when the extraction time exceeded 50 min (Fig. 1b). The optimum yield of PPP was obtained when the extraction time was 60 min. Ultrasound facilitated the polysaccharides inside the cells release to the exterior solvent. Most of the polysaccharides in broken cells released at the early period of extraction, and the yield of PPP increased in the first 60 min. However, long extraction time induced the degradation of polysaccharides, and the yield of PPP decreased. Thus, the optimum extraction time was 60 min. 3.1.3. Effects of extraction temperature on the yield of PPP To study the effect of different temperature on the yield of PPP, extraction process was carried out using the different temperatures of 40, 50, 60, 70, 80 °C. The ratio of water to raw material was fixed at 20:1 ml/g, extraction time was fixed at 60 min, and ultrasonic power was fixed at 140 W. As shown in Fig. 1c, the extraction temperature displayed a positive linear effect on the yield of PPP when the temperature ranged from 40 to 60 °C, and then the yield decreased with increasing temperature. The result was related to two main physical phenomena in UAE: acoustic cavitation and diffusion through the cell walls. The two phenomena were significantly enhanced by the extraction temperature. With the increase of temperature, surface tension and viscosity of liquid

Table 3 Estimated regression model of relationship between response variable (yield of PPP) and independent variables (X1, X2, X3, X4). Variables

SS

DF

MS

F-Value

P-Value

X1 X2 X3 X4 X1X2 X1X3 X1X4 X2X3 X2X4 X3X4 X21 X22 X23 X24

2.08 0.69 3.0  104 9.633  103 0.070 0.18 0.87 0.91 9.0  104 0.046 2.72 1.82 1.53 3.83

1 1 1 1 1 1 1 1 1 1 1 1 1 1

2.08 0.69 3.0  104 9.633  103 0.070 0.18 0.87 0.91 9.0  104 0.046 2.72 1.82 1.53 3.83

163.53 54.26 0.024 0.76 5.51 13.85 68.62 71.59 0.071 3.63 213.63 142.76 120.17 300.76

<0.0001 <0.0001 0.8804 0.4003 0.0354 0.0026 <0.0001 <0.0001 0.7946 0.0792 <0.0001 <0.0001 <0.0001 <0.0001

medium is reduced, and the vapor pressure of liquid is increased, so that it is easy to form cavitation bubbles between the liquid medium, and hence produce ultrasonic cavitation in the lower the intensity of ultrasonic wave. Cavitation effect increases cellular damage, promote intracellular polysaccharide substance spread outward, the polysaccharide content and polysaccharide extraction yield increased. Therefore, the yield of polysaccharides increased with the higher temperature. However, high temperature led to the decrease of surface tension and the increase of vapor pressure within micro bubbles, causing the damping of the ultrasonic wave (Zhao, Kwok, & Liang, 2007). This tendency is in agreement with reports of other authors in ultrasound-assisted extracting polysaccharides (Ying, Han, & Li, 2011). Thus, the yield of polysaccharides decreased when the extraction temperature was over 60 °C. Based on these results, 60 °C was considered to be optimal in the present experiment. 3.1.4. Effects of ultrasonic power on the yield of PPP Ultrasonic power is another factor that can influence extraction efficiency (Ying et al., 2011; Zhou, Yu, Zhang, He, & Ma, 2012). The effect of ultrasonic power on the yield of PPP was studied with the extraction temperature of 60 °C, extraction time 60 min, ratio of water to raw material 20:1 ml/g. From the Fig. 1d, we can see that the yield of PPP increased significantly with increasing ultrasonic power, and then decreased when the ultrasonic power was over 140 W. It was well known that the ultrasonic power facilitated the disruption of cell walls. A larger yield of polysaccharides occurred with the stronger ultrasonic power at the early period. However, acoustic cavitation in UAE yielded hydroxyl radicals, leading to chemical decomposition (Koda, Kimura, Kondo, & Mitome, 2003; Li, Pordesimo, & Weiss, 2004). More chemical decompositions were generated with the stronger extraction power. The yield of PPP decreased after 140 W. Therefore, 140 W was considered to be the optimum extraction power. 3.2. Optimization of the extraction parameters of PPP 3.2.1. Statistical analysis and the model fitting Response surface optimization is more advantageous than the traditional single parameter optimization in that it saves time, space and raw material. According to the value obtained in the single factor experiment and method of Box–Behnken designed experiment, RSM was applied to monitor the extraction characteristics of polysaccharides components in pomegranate peel and to determine the optimum conditions. There were a total of 30 runs for optimizing the four individual parameters in the current Box– Behnken design. The current design was applied to the production of PPP by ultrasound-assisted extraction. The data were analyzed by multiple regression analysis using the Design-Expert 8.0.5 and the following polynomial equation was derived to represent PPP yield as a function of the independent variables tested. Where Y is the predicted PPP yield and X1, X2, X3 and X4 are the coded values for ratio of water to raw material, extraction time, extraction temperature and ultrasonic power, respectively. Table 1 shows the process variables and experimental data. The results of the analysis of variance, goodness-of-fit and the adequacy of the models were summarized. The percentage yield range from 11.29% to 13.76%. The maximum yield of PPP (13.76%) was recorded ratio of water to raw material 20 ml/g, extraction time 60 min, extraction temperature 60 °C, ultrasonic power 140 W. The application of RSM suggested, based on parameter estimates, an empirical relationship between the response variable (extraction yield of PPP) and the test variable under consideration. Multiple regression analysis of the experimental data yielded the following second-order polynomial stepwise equation:

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Fig. 2. Response surface (3D) showing the effect of different extraction parameters (X1: ratio of water to raw material, ml/g; X2: extraction time, min; X3: extraction temperature, °C; X4: ultrasonic power, W) on the response yield.

Y ¼ 13:64 þ 0:42X 1 þ 0:24X 2  5  103 X 3 þ 0:028X 4  0:13X 1 X 2  0:21X 1 X 3 þ 0:47X 1 X 4  0:48X 2 X 3 þ 0:015X 2 X 4  0:11X 3 X 4  0:63X 21  0:51X 22  0:47X 23  0:75X 24

ð3Þ

ANOVA was used to analyze the model for significance and suitability, and a statistical summary was given in Table 2. As can be seen, the model F value of 66.96 with a low probability P value (<0.0001) indicates high significance of the model. The coefficient of determination (R2) was the proportion of variability in the data explained or accounted for by the model (Xu et al., 2013). For the model fitted, the coefficient of determination (R2) was 0.9863, indicating that only 1.37% of the total variations was not explained by the model. F-value for the lack of fit was insignificant (P > 0.05) thereby confirming the validity of the model. The value of the adjusted determination coefficient (adjusted R2 = 0.9716) also confirmed that the model was highly significant. At the same time, a very low value 0.89 of coefficient of the variation (CV) clearly indicated a very high degree of precision and a good deal of reliability of the experimental values. The model was found to be adequate for prediction within the range of experimental variables. The regression coefficient values of Eq. (3) were listed in Table 3. The P-values were used as a tool to check the significance of each coefficient, which in turn might

indicate the pattern of the interaction between the variables. The smaller the value of P was, the more significant the corresponding coefficient was (Guo, Zou, & Sun, 2010). It can be seen from this table that the linear coefficients (X1 and X2), the quadratic term coefficients (X21, X22, X23, X24) and cross product coefficients (X1X2, X1X3, X1X4, X2X3) were significant, with very small P-values (P < 0.05), whereas the linear coefficients (X3 and X4) and the cross product coefficients (X2X4 and X3X4) had no significant influence (P > 0.05) on the extraction yield of PPP. By observing linear and quadratic coefficients, we concluded that the order of factors influencing the response value of the extraction yield of PPP was as follows: ratio of water to raw material > extracting time > ultrasonic power > extraction temperature. The full model filled Eq. (3) was made three-dimensional and contour plots to predict the relationships between the independent variables and the dependent variables. 3.2.2. Response surface analysis Three-dimensional (3D) response surfaces and two-dimensional (2D) contour plots were the graphical representations of regression function. They are presented in Figs. 2 and 3 for the independent variables (ratio of water to raw material, extraction time, extraction temperature and ultrasonic power) were obtained by keeping two of the variables constant, which indicated the changes in extraction yield under different conditions.

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Fig. 3. Contour plots (2D) showing the effect of different extraction parameters (X1: ratio of water to raw material, ml/g; X2: extraction time, min; X3: extraction temperature, °C; X4: ultrasonic power, W) on the response yield.

The 3-D plot and the contour plot in Figs. 2a and 3a, which give the extraction temperature and ultrasonic power (0 level), show that extraction yield of PPP increased evidently with increasing of ratio of water to raw material from 15 to 21 ml/ g, but beyond 21 ml/g, the extraction yield of PPP increased slowly as the ratio of water to raw material ascended. The

extraction yield of PPP increased evidently with increasing of extraction time from 50 to 63 min, but beyond 63 min, the yield of PPP increased slowly as the time ascended. Figs. 2b and 3b show that the 3-D plot and the contour plot at varying ratio of water to raw material and extraction temperature at fixed extraction time and ultrasonic power (0 level). From Figs. 2b

C.-p. Zhu et al. / Food Chemistry 177 (2015) 139–146

and 3b, it can be seen that the extraction yield of PPP increased evidently with increasing of extraction temperature from 50 to 60 °C, but beyond 60 °C, the yield of PPP decreased slowly as the temperature ascended. The 3-D plot and the contour plot based on independent variables ratio of water to raw material and ultrasonic power were shown in Figs. 2c and 3c, while the extraction time and temperature were kept at a zero level. An increase in the extraction yield of PPP could be significantly achieved with the increase of ratio of water to raw material. It was obvious that the extraction yield of PPP was increased rapidly with the increasing ultrasonic power from 120 to 141 W, but beyond 141 W, the extraction yield of PPP decreased slowly as the ultrasonic power ascended. The 3-D plot and the contour plot in Figs. 2d and 3d, which give the ratio of water to raw material and ultrasonic power (0 level), show that extraction yield of PPP increased evidently with increasing of extraction time from 50 to 63 min, but beyond 63 ml/g, the extraction yield of PPP decreased slowly as the extraction time ascended. The extraction yield of PPP increased slowly with increasing of extraction temperature from 50 to 60 °C, but beyond 60 °C, the yield of PPP decreased slowly as the temperature increased. From Figs. 2e and 3e, we can see that the extraction yield of PPP was increased rapidly with the increasing ultrasonic power from 120 to 141 W, but beyond 141 W, the extraction yield of PPP decreased evidently as the ultrasonic power increased. The yield of PPP is also increasing slowly with the extraction time before 60 min, and then decreased slowly after 60 min. The Figs. 2f and 3f show that when ratio of water to raw material and extraction time were at a certain value, the extraction of PPP obviously increased with ultrasonic power added. In addition, when ultrasonic power was unchanged, the extraction yield of PPP increased as the temperature increased. 3.2.3. Optimization and verification By using Design Expert 8.0.5 software, the optimum condition was obtained and recommended as a practical optimum: ratio of water to raw material, 24.29 ml/g; extraction time, 63.27 min; extraction temperature, 54.51 °C; and ultrasonic power, 147.74 W. The estimated values for Y, 13.787% was obtained at those conditions. To further test the reliability of the experimental method, the extraction experiment was carried out by adopting the program of optical analytical model. Adjusted extraction conditions are: ratio of water to raw material, 24 ml/g; extraction time, 63 min; extraction temperature, 55 °C; and ultrasonic power, 148 W. A mean value of 13.658 ± 0.133% (n = 3) was gained, obtained from real experiments, demonstrated the validation of the RSM model. The results of analysis confirmed that the response model was adequate for reflecting the expected optimization, and the model of Eq. (3) was accurate and reliable. In contrast to traditional techniques, this model takes into account the interactions among several independent variables. 4. Conclusion In the present paper, the ultrasound-assisted extraction of PPP from pomegranate peel was performed with a four-variable, three-level Box–Behnken design based on the RSM. The method proved to be useful for optimization of ultrasound-assisted technology of PPP extraction. The statistical analysis showed that ratio of water to raw material of 24 ml/g, extraction time of 63 min; extraction temperature of 55 °C, and ultrasonic power of 148 W were the best conditions to produce PPP. Under the most suitable conditions, the experimental yield of PPP was 13.658 ± 0.133%, which was closed with the predicted yield value 13.787%.

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Acknowledgments This work was supported by National Natural Science Foundation of China (No. 31301598), Natural Science Foundation of Shaanxi Province (No. 2012JQ3014), and the Fundamental Research Fund for the Central Universities of China (Nos. GK201402042 and GK201304007). References Adams, L. S., Seeram, N. P., Aggarwal, B. B., Takada, Y., Sand, D., & Heber, D. (2006). Pomegranate juice, total pomegranate ellagitannins, and punicalagin suppress inflammatory cell signaling in colon cancer cells. Journal of Agricultural and Food Chemistry, 54, 980–985. Adhami, V. M., & Mukhtar, H. (2006). Polyphenols from green tea and pomegranate for prevention of prostate cancer. Free Radical Research, 40, 1095–1104. Bachoual, R., Talmoudi, W., Boussetta, T., Braut, F., & El-Benna, J. (2011). An aqueous pomegranate peel extract inhibits neutrophil myeloperoxidase in vitro and attenuates lung inflammation in mice. Food and Chemical Toxicology, 49, 1224–1228. Bolling, B. 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