Pre Treatment Optimization of Recovery of Polyphenols from Pink Guava Fruit Processing Wastes by Membrane Technology

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1 Pre Treatment Optimization of Recovery of Polyphenols from Pink Guava Fruit Processing Wastes by Membrane Technology Lilis Sukeksi, Che Rosmani Che Hassan, Nik Meriam Sulaiman, and Mohamed Kheireddine Aroua. Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia. Abstract The optimum conditions for the pretreatment of polyphenols from pink guava fruit processing wastes by membrane technology were determined using response surface methodology (RSM). The design based on two level full factorial designs was applied to identify the optimal flux and total polyphenols content during pretreatment. The experimental design is used to investigate the effect of pressure (1-3 Bar), flow rate (2-6, no of pump rate), and concentration of sample (20-60 g/l). Experimental design, was conducted using the software Design Expert (Version 7 trial, Stat-Ease Inc., Minneapolis, MN, USA). Three repetitions at the centre design point were performed to estimate the pure error sum of squares. The pressure (B), concentration (A), Flow rate (C), the interaction between concentration and flow rate (AC), pressure and flow rate (BC) and concentration and pressure (AB) are significant to the response of the flux yield, meanwhile there are no terms which are significant in the response to the total phenolics content effects. The most desirable operating conditions showed at concentration of 20 g/l, total membrane pressure of 3 Bar and flow rate of 6. This optimum membrane operating condition with of desirability value was expected to be able to produce high flux and total phenolics content (TPC). Keywords: waste, polyphenols, extraction, solvent and membrane. 1. Introduction Fruits and vegetables are the main sources of antioxidant such as polyphenols, ascorbic acid and others that may help in reducing the risk of degenerative diseases such as cancer, arteriosclerosis and heart disease (Halliwell, 1996). Processing of fruits to produce fruit juice will result in significant amounts of residue or by-products with rich antioxidant or polyphenols content. Exploiting the use of these by-products, to produce of food additives or supplements with high nutritional value has gained some attentions. One of the most effective options is the recovery of bioactive plant food constituents, which could be utilized in the pharmaceutical, cosmetics and food industry (Makris, 2007). A number of studies on 1

2 residual sources of antioxidants have increased considerably in recent years (Moure, 2001). In Malaysia, many edible tropical fruits are processed into natural and concentrated juices, jellies, pulp and extracts. In these processes, seeds, peels and other undesired parts are routinely discarded, causing various environmental problems. Our previous study (Sukeksi, 2008) showed that pink guava residue processing is a good source of natural antioxidants. Therefore, the recovery of polyphenols from pink guava processing residue is of great importance, not only for their properties but also for the exploitation of large amount of pink guava industry residue which is mainly used nowadays as cattle feed, soil conditioning, or trucked away to disposal sites. Consequently, their environmental impact can also be mitigated. Efficient, inexpensive and environmentally-friendly use of agro-food industry waste is highly cost-effective and can minimizes environmental impact. Polyphenols compounds are common dietary phytochemicals founds in fruits and vegetables. Most of the beneficial characteristics of polyphenols compounds have been recognized for their antioxidant characteristics which is a fundamental lifeimportant property (Rice-Evans, 1997). The composition of polyphenols depends on the solvent of extraction as found from previous study (Sukeksi, 2008), whereby 60% methanol/water solution is considered to be the best for polyphenols extraction. This extraction procedure is efficient, but the extracts are not safe for human consumption due to potential toxic effects from the residual organic solvent. A range of solvents, such as hexane and methanol combinations (Buelga, 1995), ethanol-benzene combinations (Kofujita, 1999) and ethyl acetate (Bonilla, 1999) have been reportedly used (Cacace, 2002). All of these solvents are toxic to human if consumed in large dose. Health concerns have sparked research into methods that would reduce the use of organic solvents in the extraction procedure. Purification of polyphenols after extraction procedure is often carried out using conventional techniques such as evaporation which is not suitable to concentrate polyphenols due to temperature-prone properties. Membrane processing is one method that eliminates the use of toxic organic solvents to concentrate the final product. In order to produce a high quality extract with antioxidant activity suitable for food, cosmetic or pharmaceutical industry, water solvent is used for extraction. For this purpose, it is necessary to purify the extract obtained and thus, removing all the inert and undesirable components by pretreatment using membrane ultra-filtration (UF) before purification of polyphenols. Ultra-filtration (UF) is the most commonly used procedure to separate some components from a solution and is determined by the molecular weight cut-off (MWCO) or rejection of membrane. UF has been used in the past for the removal of polyphenols from grape musts or wines (Moresi, 2003). In the separation of polyphenols using membrane technology, several variable factors are known to influence the separation. In general, optimization of such process could be achieved by either empirical or statistical methods. Therefore, the objective of this pretreatment study is to examine significant and determining parameters, and to optimize the pretreatment of popyphenols recovery from pink guava fruit processing wastes by membrane UF. Response surface methodology (RSM) which, originally described by (Box, 1951), enables evaluation of the effects of several process variables and their interactions 2

3 on response variables. RSM has been successfully used to model and optimize biochemical and biotechnological processes related to food systems (Cacace, 2002). 2. Materials and Equipment Commercially tubular PVDF membrane type FP 200 with nominal MWCO 200,000 MW and high solvent resistance for pretreatment was used. The membrane was manufactured and supplied by PCI Membrane Technology. Prior to use, membrane was soaked overnight in water to eliminate and left over impurities from the mechanized processes or additives used for stabilization and wetting of the membrane. The equipments used consist of a pump, feed reservoir, permeate collection reservoir, inlet and outlet pressure gauges, flow rate indicator, pressure control valve, and a balance equipped data processing monitor. Master flex flexible tubing was used as connection. Folin-Ciucalteu and Gallic acid were supplied by Sigma-Aldrich (Germany) and Sodium Carbonate powder was supplied by Fluka (Germany). The pink guava processing waste was collected from pink guava the fruit farm located in Sitiawan, Perak, (Malaysia), which were produced from Decanter and Refiner with 50 % composition for both. 3. Methods 3.1. Sample preparation and extraction Solvent extraction is the most common technique for extraction of compounds found in fruit, as well as polyphenols. The extraction is highly dependent on the solvent, due to the variant polyphenols potential with compound with different polarity. The choice of solvent is the first and basic consideration for solid-liquid extraction process. Polyphenols in the pink guava waste extracted using water as solvent with 1000 ml of solvent and 20, 40 and 60 g of raw material. Polyphenols are used as health supplement and water was chosen as the solvent as water is considered safe to be consumed. The mixture was then constantly blended for 10 minutes until uniform slurry or solution was obtained by using a Warring blender. After allowing 12 hours at room temperature, the aqueous extract was separated from the cake by filtering using muslin cloth and centrifuging to reduce the suspended solid content. The solution was subjected to ultra-filtration runs using membrane FP 200 as pretreatment Determination of total phenolics content The total phenolics content was determined in all samples by using the Folin- Ciocalteau method, which is adapted from (Singleton, 1965) with some modifications. The range between mg/ml Gallic acid calibration standard solution was prepared by accurately weighing and dissolving with distilled water (solvent). The reaction mixture consisted of 200 μl extract of samples which was mixed with 1.5 ml of Folin Ciocalteau reagent was allowed to stand at room temperature for 5 minutes before adding 1.5 ml of sodium bicarbonate. After standing for 90 minutes at room 3

4 temperature, absorbance was measured at 760 nm. Results are expressed as mg/ml Gallic acid equivalents (GAE) Statistical analysis For most bioprocess technologies, there are no theoretical models which can be used to explain the process performance (Haaland, 1989). Consequently, successful research is characterized by effective empirical problem solving. Statistical problem solving provides a set of powerful tools which can be used to maximize the efficiency and productivity of empirical problem solving. In this study, the effects of operating parameters were optimized using response surface methodology (RSM). RSM is essentially a particular set of mathematical and statistical methods suitable for designing experiments, building models, evaluating the effects of variables, and searching for optimum conditions of variables to predict targeted responses. It is an important branch of experimental design and a critical technology in developing new processes, optimizing their performance, and improving design and formulation of new products. Its great applications particularly have been in situations whereby a large number of variables influencing the system feature. This feature termed as the response and normally measured on a continuous scale, which represents the most important function of the systems (Ozer, 2008). Response surface procedures are not only primarily used for the purpose of allowing the researchers to understand the mechanism of the system or process, but also to determine the optimum conditions or a region for the factors at a certain operating conditions (Mune, 2008). The application of statistical experimental design techniques in sorption processes could result in improved product yields, reduced process variability, closer confirmation of the output response to nominal and targeted requirements, as well as reduced development time and overall costs (Ozer, 2008). The optimization of this process has been conducted with the use of commercial statistical software called, Design-Expert release 7. The results show the half normal plot in which ranks of the absolute value of various responses are identified. From the half normal plot, one can analyze the significant of each parameter and their combinations which are given by a negative or positive value. The effects of the factor are calculated by averaging the responses of each factor at the plus levels and subtracting the average at the minus levels for the same factor (Abdul-Wahab, 2007). Negligible effects are normally distributed, with zero mean and variance, σ 2. They tend to fall along a straight line on this plot, whereas the significant effect will have nonzero means and will not lie along the straight line (Montgomery, 2005). Differences between variables were tested for significance using the one-way of analysis of variance (ANOVA) procedure (SPSS for Window 13.0, SPSS Inc. Chicago, IL; Design Expert 7 trial, Stat-Ease Inc. Minneapolis, MN, USA). Significant differences were accepted at P The use of statistical design to develop clear and manageable experiments has been well documented. The design of experiments (DOE) approach especially with factorial designs has been very popular. Factorial designs are applied to investigate 4

5 the effects of several factors on one or more response variables since they quickly identify those few factors that affect assay performance (Montgomery, 2005). A special case of factorial designs are when the factors of interest have only two-level, usually designated as 2 k factorial design, where k denotes the number of factors under consideration. Two-level, full factorial design is very powerful because they provide information about all of the main effects and two-factor interactions. These designs will help to screen many factors to discover the vital few, and perhaps how they interact. Previously, a two level full factorial design was applied to identify the optimal flux and total polyphenols content during pretreatment for recovery of polyphenols from pink guava fruit processing waste by membrane technology. The experimental design was used to investigate the effect of pressure (1-3 Bar), flow rate (2-6, No of pump rate), and concentration of sample (40-60 g/l) fluid extraction from pink guava waste and water as a solvent on flux and total polyphenols content yields. Experimental design and data analysis were conducted using the software Design Expert (Version 7 trial, Stat-Ease Inc., Minneapolis, MN, USA). Three repetitions at the centre design point were performed to estimate the pure error sum of squares. 4. Results and Discussion 4.1. Experimental responses models The three factors of lower, middle and upper design point for RSM in coded and natural value and the results of the 19 runs, showing each condition of the experiment runs (concentration between waste and solvent, trans-membrane pressure and flow rate) and the responses to the flux and total phenolics content are shown in Table 1. In RSM, natural variables are transformed into coded variables that have been defined as dimensionless (Myers, 2002). After creating the 2 k full factorial design, the 19 designed experiments were performed. The experimental data were mathematically processed and some experimental responses models were produced. For the model fitted, software generates model coefficients, R 2 -values, F-values and significant probabilities and hence, one can justify the significance of each experimental variable. After analysis was done, the final optimization on the three factors, which includes concentration between waste and solvent, trans- membrane pressure and flow rate, were obtained. The optimal operating condition of the casting process was determined by those experimental responses models. 5

6 Table 1: Selected factors and their responses in the experimental design pretreatment of recovery of polyphenols from pink guava fruit processing by using membrane technology. Factor 1 Factor 2 Factor 3 Response 1 Response 2 Run A: Concentration B: Pressure C: Flow Rate R1 R2 (g/l) (Bar) (No at Pump) Flux TPC at Perm. 1 (-1)20 (-1)1 (-1) (-1)20 (-1)1 (-1) (+1)60 (-1)1 (-1) (+1)60 (-1)1 (-1) (-1)20 (+1)3 (-1) (-1)20 (+1)3 (-1) (+1)60 (+1)3 (-1) (+1)60 (+1)3 (-1) (-1)20 (-1)1 (+1) (-1)20 (-1)1 (+1) (+1)60 (-1)1 (+1) (+1)60 (-1)1 (+1) (-1)20 (+1)3 (+1) (-1)20 (+1)3 (+1) (+1)60 (+1)3 (+1) (+1)60 (+1)3 (+1) (0)40 (0)2 (0) (0)40 (0)2 (0) (0)40 (0)2 (0) Experimental Design Using the Design Expert software, the significant factors have to be selected before ANOVA analysis. The analysis for this assay is using the half normal plot for the response variables of concentration between waste and solvent, trans-membrane pressure, and flow rate respectively. The factors and responses were selected for the three factors two yields because they were the main factors and yield in this study. The squares should split into two groups; those on the left (they should fall on a relatively straight line originating from zero) were pooled into the error group. Those on the right (they should fall off the line), wold be included in the model when they 6

7 H a lf - N o r m a l % P r N o r m a l % P r o were selected. Figure 1 shows the graph of half normal plot and normal plot of flux and total phenolics content. Factors that lie along the straight line were negligible and the rest of the factors and their cross interaction were significant. In Figure 1a, it can be seen that the pressure (B), concentration (A), Flow rate (C), the interaction between concentration and flow rate (AC), pressure and flow rate (BC) and concentration and pressure (AB) are significant to the response of the flux yield. Meanwhile from the Figure 1b, the factors B and C have positive effects on the analysis, meaning that, increasing these effects setting will increase the yield. Factor A with the others combination of factors AC, BC and AB gave the negative effect. This can be interpreted to as AC, BC and AB were increased, the yield would decrease. Usually, the error points would fall at the left hand side. The combinations of factors ABC fall on the straight line originally from zero, giving the error reading. Hence, it suggest that from the analysis on half-plot for flux yield that A, B, C are significant. In addition, the interaction of all these factors, namely AC, AB, and BC are significant. Therefore, they are pursued for further analysis. Based on Darcy law, pressure is the driving force for mass transfers through the membrane. Hence, the average of permeate flux is therefore expected to be influenced by trans-membrane pressure. Based on Figure 1c, there are no terms which are significant in the response to the total phenolics content effects, i.e. the concentration (A), pressure (B), flow rate (C) and for all interactions between them. In the first analysis, the software automatically suggested the use of linear model to represent the relationship of the independent parameters and responses. In two level factorial designs, the normal probability plot of residual are investigated to diagnose the adequacy of the existing model. The normal probability plot will indicate whether the residual follows a normal distribution or not. In this case, the points will follow a straight line. From the Figure 1d, there are no factors have the positive and negative effects on the analysis. This can be interpreted as no factors can influence the yield of total phenolics content. Design-Expert Software Flux Half-Normal Plot Design-Expert Software Flux Normal Plot Error from replicates A: Concentration B: Pressure C: Flow Rate Positive Effects Negative Effects BC AB AC C A B A: Concentration B: Pressure C: Flow Rate Positive Effects Negative Effects AC AB BC C B A a Standardized Effect b Standardized Effect 7

8 N o r m a l % P r o N o r m a l % P r o H a lf-n o rm a l % P ro b N o r m a l % P r o Design-Expert Software TPC Error from replicates A: Concentration B: Pressure C: Flow Rate Positive Effects Negative Effects Half-Normal Plot BC AC ABC Design-Expert Software TPC Shapiro-Wilk test W-value = p-value = A: Concentration B: Pressure C: Flow Rate Positive Effects Negative Effects Warning! No terms are selected. Normal Plot B C A AB c Standardized Effect d Standardized Effect Fig. 1.Graph of half normal plot and normal plot of flux and total phenolics content Figure 2a and 2b represent the normal probability of residual of the two responses, i.e. flux and total phenolic content, respectively. Based on these plots, it seems that in the case of both the flux and total phenolic content, there is no severe indication of non normality. They show that the point in the plot seems fall along the straight line, in which, this indicates the data were normally distributed. Design-Expert Software Flux Normal Plot of Residuals Design-Expert Software TPC Normal Plot of Residuals Color points by value of Flux: Color points by value of TPC: Internally Studentized Residuals Internally Studentized Residuals Figure 2: 2a and 2b are the normal probability of residual of the two responses flux and total phenolic content Analysis of Variance (ANOVA) From the response and model diagnostic analysis, the analysis of variance (ANOVA) was performed to interpret the optimization result. In this case, the best fit model for each response, flux and total phenolic content and ANOVA result are given in Table 2 and 3 respectively. 8

9 Table 2: Analysis of Variance (ANOVA) for yield flux. Source Sum of Squares df Mean Square F Value p-value (P>F) Model < A-Concentration < B-Pressure < C-Flow Rate < AB AC BC ABC Curvature < Lack of Fit Pure Error Cor Total According to the Design Expert software, the Model F-value was tested to compare model variance with residual (error variance). If the variance is approximately the same, the ratio will be equivalent to one and it is less likely that any of the factors have a significant effect on the response. Based on the data given in Table 2 model F-value for the flux response implies F-values of and the model was significant. There was only a 0.01% chance that a Model F-value this large could occur due to noise. Values of Prob>F less than indicate that the model terms are significant. In this case A, B, C, AB, AC, BC were significant model terms. Values of Prob>F greater than indicate the model terms are not significant. If there were many insignificant model terms exist (not counting those required to support hierarchy), model reduction may improve our model. The Curvature F-value of implies that there is significant curvature (as measured by difference between the average of the center points and the average of the factorial points) within, the design space. There is only a 0.01% chance that a Curvature F-value could occur due to noise. Statistical methods can be used to separate the signal from the noise. The noise is all of the other variation in the data (Haaland, 1989). The Lack of Fit F-value of 0.37 implies the Lack of Fit is not significant relative to the pure error. There is a 55.54% chance that a Lack of Fit F-value could occur due to noise. Non-significant lack of fit is good to fit the model. Table 3: Standard deviation, mean and R-Squared from the ANOVA for yield flux Std. Deviation 1.31 R-Squared Mean Adj. R-Squared C.v. % 4.83 Pred. R-Squared PRESS Adeq. Precision

10 Table 3 shows standard deviation, mean and R-Squared from the ANOVA for yield flux. From the table, the Pred R-Squared of is in reasonable agreement with the Adj R-Squared of Adeq Precision measures the signal to noise ratio. A ratio that is greater than 4 is desirable. The ratio of is indicating adequate signal. This model can be used to navigate the design space. The statistical model, representing the flux yield from pretreatment of pink guava fruit processing wastes by membrane technology as a function of the independent variables within the region under investigation, can be expressed by the following equation, : Y = X X X X 1 X X 1 X X 2 X 3 Where Y is the flux yield and X 1, X 2, X 3, are the actual factors for concentration, pressure and flow rate, respectively. Table 4: Analysis of Variance (ANOVA) result for Total phenolics content. Source Sum of Squares df Mean Square F Value p-value (P>F) Model 0 0 Curvature Residual Lack of Fit Pure Error Cor Total Table 4 shows Analysis of Variance (ANOVA) result for Total phenolics content. From Table 4, the Lack of Fit F-value of 1.55 implies the Lack of Fit model is not significant relative to the pure error. There is a % chance that a Lack of fit F- value could occur due to noise. Non significant lack of fit is good to fit the model. Values of Prob>F less than indicate model terms are significant. In this case there are no significant model terms. Values that are greater than indicate the model terms are not significant. The curvature F-value of 0.99 implies the curvature (as measured by difference between the average of the center points and the average of the factorial points) in the design space is not significant relative to the noise. There is a 33.34% chance that Curvature p-value could occur due to noise. Table 5: Standard deviation, mean and R-Squared from the ANOVA for yield flux Std. Deviation 3.10 R-Squared Mean Adj. R-Squared C.V. % 6.04 Pred. R-Squared Press Adeq. Precision

11 Table 5 shows standard deviation, mean and R squared from the ANOVA for yield flux. From the table a negative Pred R-Squared implies that the overall mean is a better predictor of our response than the current model. Adeq Precision measures the signal to noise ratio. A ratio of indicates an inadequate signal and we should not use this model to navigate the design space. The statistical model, representing the flux yield from pretreatment of pink guava fruit processing wastes by membrane technology is not a function of the independent variables within the region under investigation, can be expressed by the following equation,: Y = Optimization Optimization is a mean to set goals for, each response for generating the optimum setting that suite the goals. Thus, the objective of this study is to find the optimum setting for maximum flux and maximum total phenolic content. In order to achieve this, first the goal for each response has to be chosen to obtain the optimum condition using Design Expert. The goals are set in Table 6. After a series of statistical investigation, the software finally provides an optimal design with the different desirability factors. This optimal design was calculated based on the best fit models for each response generated by the software. Table 7 also shows an optimal design and its alternative solution with different desirability. The constraints used were the high and the low level of each factor, where the objective used was to maximize the flux and total phenolic content yields. All 19 solutions generated by Design Expert were investigated to see which one was the best that meets the goals. It can be seen that the most desirable operating conditions were concentration of 20 g/l, total membrane pressure of 3 Bar and flow rate of 6. This optimum membrane operating condition with of desirability value was expected to be able to produce high flux and total phenolics content (TPC). Table 6: Optimal solution for concentration, pressure and flow rate to the responses of flux and total phenolics content. Name Goal Lower Limit Upper Limit Lower weight Upper Weight Importance Concentration is in range Pressure is in range Flow Rate is in range Flux maximize TPC maximize

12 D e s ira b ility Table 7: Desirability of concentration, pressure and flow rate to the responses of flux and total phenolics content. Solutions Concentration Pressure Flow-rate Flux TPC desirability Number * (*) selected solution Design-Expert Software Desirability 1 0 X1 = A: Concentration X2 = B: Pressure Actual Factor C: Flow Rate = B: Pressure A: Concentration Figure 3: 3D Surface demonstration of the optimum setting. 3D surface is an option in Design Expert to view the result of the experiments in a graphical view. The 3D graph in Figure 3 demonstrates the optimum setting to achieve the goal/desirability in this study, the concentration between waste and solvent, the trans-membrane pressure, and the flowrate in order to get the maximum flux and total phenolic content. The higher desirability is the better of the optimum setting. 12

13 5. Conclusions Response surface methodology was used to establish the optimum process variables of Trans membrane pressure (TMP), flow rate, and ratio concentration between waste and water to responses total phenolics content (TPC) and flux for pretreatment recovery polyphenols from pink guava processing wastes. By using the Design Expert software, the significant factors have to be selected prior to ANOVA analysis. From the analysis, it can be concluded that pressure (B), concentration (A), flow rate (C), the interaction between concentration and flow rate (AC), pressure and flow rate (BC) and concentration and pressure (AB) are significant to the response of the flux yield and there are no terms which are significant in the response to the total phenolics content effects. Meanwhile, factors B and C have positive effects on the analysis of flux, meaning that by increasing these effects setting, yield will be increase. Factor A with the others combination of factors AC, BC and AB give the negative effect that can be interpreted to as the AC, BC and AB are increased, the yield will be decrease. For the analysis of total phenolics content, there are no factors that have positive and negative effects on the analysis and it can be interpreted as no factors can influence the yield of total phenolics content. The optimum set of operating variables can be obtained in order to achieve the desired pretreatment for recovery polyphenols from pink guava processing wastes. Therefore, it was recommended that the pretreatment condition be concentration 20 g/l, pressure 3 Bar, and flow rate at 6 with the flux result of Lm -2 h -1, and total phenolics content of 51.55%. Acknowledgements The work was supported by grants from the MOSTI Science Fund ( ). References Abdul-Wahab, S. A. (2007). Prediction of tropospheric ozone concentrations by using the design system approach. Journal of environmental Science & Health. Part A-Toxic- Hazardous Subtances & Environmental Engineering, Bonilla, F. M. (1999). Extraction of phenolic compounds from red grape marc for use as food lipid antioxidants. Food Chemistry, 209. Box, G. E. (1951). On the experimental attaintment of optimum conditions. Journal of the Royal Statistical Society., Buelga, C. S.-A.-B. (1995). Comparative flavon-3-ol composition of seeds from different grape varieties. Journal Food Chemistry, Cacace, J. E. (2002). Extraction of anthocyanins and other phenolics from black currants with sulfured water. Journal Agriculture Food Chemistry, Haaland, P. D. (1989). Experimental Design in Biotechnology. New York: Marcel Dekker, Inc. Halliwell, B. (1996). Antioxidants in human health and disease. London, UK.: Neurodegenerative Disease Research Centre, King's College London. 13

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