Optimizing conditions for enzymatic clarification of prebiotic extracted jackfruit seeds using response surface methodology
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1 The 5 th PSU-UNS International Conference on Engineering and Technology (ICET-0), Phuket, May -, 0 Prince of Songkla University, Faculty of Engineering Hat Yai, Songkhla, Thailand 90 Optimizing conditions for enzymatic clarification of prebiotic extracted jackfruit seeds using response surface methodology Bancha Lolharat, Sininart Chongkhong *, Pakamas Chetpattananondh Prince of Songkla University, Faculty of Engineering, Hat-Yai, Thailand *Authors to correspondence should be addressed via csininart@yahoo.com Abstract: This work was initiated to optimize the enzymatic clarification process of prebiotic extracted jackfruit seeds using response surface methodology (RSM). The seeds were treated with α-amylase at various enzyme concentrations ( 0.%), temperatures ( 00 C) and time of treatment ( 40 min). The effects of these enzymatic treatments on the amount of reducing sugar were studied by employing a second order central composite design. The determination coefficient, R values for reducing sugar were greater than 0.9. Statistical analysis showed that reducing sugar was significant. Base on response surface, the optimum conditions for clarifying the seeds were the enzyme concentration of 0.7%, incubation temperature of C and time of 40 min. In this case, the highest amount of obtained reducing sugar was.04 g/l. Key Words: Jackfruit seeds / α-amylase/ Enzymatic Hydrolysis / Response surface methodology (RSM). INTRODUCTION Starch has become an important biopolymer which is used as a feedstock material in many industries. The nutritive values of the jackfruit seeds are 9.% amylose, 9.5% crude proteins,.94% fat,.% ash, 8.57% moisture and ph-6.69 []. For several industrial processes, enzymes are used to transform starch molecules to be useful. Alpha-amylase (α-amylase) enzyme is also widely used in food and textile industries. Liquefaction is a dispersion process of the insoluble starch granules in aqueous solution that a partial hydrolysis reaction occurs by the thermo stable amylases. The liquefaction is the first and the most important steps in starch molecule structure transforming process. Its purpose is to be lower the viscosity of the pretreated product which is stable by no byproducts and suitable for further processes, i.e saccharification []. The gelation and retrogradation of the products can be prevented by using suitable alpha-amylase amount. If the amylase amount is insufficient, the subsequent yields from saccharification and fermentation processes will be decreased []. The enzymatic hydrolysis process provides a higher yield than acid and alkaline hydrolysis processes [4]. As an only one step of physical pretreatment methods is typically not adequate, the enzymatic pretreatment is interested. The enzymatic hydrolysis of starch has been practiced on an industrial scale. Moreover, the used enzymes can be recovered and recycled [5]. The crude α-amylase, which is produced from Bacillus subtilis, can hydrolyze efficiently various starchy substrates [6]. Statistical approaches can help to enhance product yield and reduce the cost of production [7]. Response surface methodology (RSM) has been widely used to evaluate and understand the interactions between different physiological and nutritional parameters. It is an efficient mathematical approach widely applied in the optimization of the fermentation process, enzymatic hydrolysis and other metabolites [8 0]. The optimum conditions for enzymatic hydrolysis of cassava waste with α-amylase was at ph 5.5, 00 o C for hrs followed with gluco-amylase at ph 4.5, o C for 4 hrs. []. The aim of the present work is to establish the optimum conditions (incubation time, temperature and enzyme concentration) for enzymatic clarification of prebiotic extracted jackfruit seeds using RSM.. MATERIALS AND METHODS.. Materials and Microorganism Prebiotic extracted jackfruit seeds, their components are shown in Table, obtained from SME- OTOP research group, Prince of Songkla University, Thailand. Alpha-amylase, Aspergillus cryzae, was bought from the Sigma-Aldrich company.
2 Table. The components of prebiotic extracted jackfruit seeds. Component Prebiotic extracted jackfruit seeds (%w) Protein 4.99% Crude fat 0.% Moisture 58.8% Ash 0.75% Crude fiber.0% Total carbohydrate 5.0% Energy 6.8kcal Total sugar 0.40%.. Pretratment and Enzymatic hydrolysis The jackfruit seeds which were crushed to be mm. size, were boiled at 85 C for 5 min. Then they were cooled down to a room temperature before a hydrolysis step. 5 g of the boiled seeds mixed with 5 ml water were hydrolyzed in 50 ml screw-capped bottles with 0. %w α-amylase. The liquefaction was investigated at 00 C for 40 min. The reactions were kept at ph 6.0 by a sodium acetate buffer. The hydrolysates were separated by centrifuge at 5000 rpm for 5 min. before reducing sugars analysis... Analytical methods The reducing sugars were assayed in terms of glucose by DNS method using a double beam UV-Vis spectrophotometer (model HP 5) with UV-Visible ChemStation software []..4. Experimental design and optimization Central composite design (CCD) was used to find the optimum reducing sugar concentrations in the hydrolyzed products. Time (, min), temperature (, C), and α-amylase amount (, %w) were chosen as the independent variables (Tables and ). The variables were coded according to Eq. (): i xi xi, i =,,,, k () x j where x i is the dimensionless coded level of variables, i is the actual value, x i is the average of the high and low level values, x and is the high value minus the low j value of that the variables. The significance of each variable influencing hydrolysis process was determined by Student s t test with 95% confidence levels. Table. Variables in the experimental design. Variables Coded levels Temperature ( C) Alpha-amylase (%w) Table. The central composite design matrix employed for three independent variables (Actual values given in Table ). Run no. x x x sugar concentration (Y, g/l) in the hydrolyzed product was a desirable output. To obtain optimum conditions, 7 experiments in Table were studied. A mathematic model for predicting the output yields by ANOVA, Eq. (), was created from the roles of parameters, their interactions and statistical analysis. Y b b 0 b b b b b b b b Where Y is the predicted response;,, are the independent variables; b 0 is the offset term; b, b, b are the linear effects; b, b, b are the square effects; and b, b, b are the cross effects of the interaction terms. A correction of the polynomial model Eq. () can be verified with the determination coefficient (R ) and the F-test for the statistical significance.. RESULTS AND DISCUSSION.. Response surface analysis for optimization of three factors The trials and results for central composite design are shown in Table 4. The response surface and contour curves were obtained by using Eq. (): Y () ()
3 Table 4. Experimental and predicted yields for reducing sugar Run no. Experimental Predicted Fig. shows a satisfactory correlation between experimental values and predictive values. As can be seen from the figure, the predicted data of the response from the empirical model agreed well with the observed ones in the range of the operating variables. Fig.. Parity plot showing the distribution of experimental vs. predicted values of reducing sugar. The results from using Eq. () showed a high determination coefficient, R = 0.9, which implied that the model was a good fit with the experiments. It gave 9.% predicting efficiency. The adjusted R value corrects the R value for the sample size and for the number of terms. A high value of adjusted determination coefficient (adjusted R = 0.8) advocates for a high significance of the model. As illustrated in Tables 5 and 6, a P value of for any factor in analysis of variance (ANOVA) indicates a significant effect of the corresponding factors on the response. The model F value of 9.08 and values of probability > F indicated that the model terms are significant. Table 5. Coefficients, t-statistics and significance probability of the model. Term Coefficient Value Standard error t-value P-value Constant b Time b Temperature b α-amylase b Time x time b Temperature x temperature b α-amylase x α-amylase b Time x temperature b Temperature x α-amylase b Time x α-amylase b Table 6. ANOVA for the full quadratic model. Source of Sum of squares Degrees of freedom Mean squares F-value Probe > F variation (SS) (DF) (MS) Regression Residual 7 7 LOF Error Pure Error Total 7.8 6
4 4.. Interactions among the factors Effects of temperature and time on the reducing sugar amount are shown in Fig.. The hydrolysis yield was increased with an increase in time. But the yield was reduced when the temperature was increased. Reduce Temperature( o C) 9 9 Temperature( o C) Fig.. Response surface and contour plot of temperature vs. time (the enzymatic hydrolysis of prebiotic extracted jackfruit seeds with 0.% α-amylase). Fig. shows the effects of α-amylase amount and hydrolysis temperature. The reducing sugar concentration in the hydrolysis product was increased with α-amylase amount from to 0. %w. However, the yields were decreased in the range of 0. to %w. And the yields were also reduced when the temperature was increased Temperature ( o C) 9 9 Temperature ( o C) Alpha amylase (%w) Alpha amylase (%w) Fig.. Response surface and contour plot of temperature vs. α-amylase amount (the enzymatic hydrolysis of prebiotic extracted jackfruit seeds for 50 min) The reducing sugar amount in relation with α- amylase amount and time is shown in Fig. 4. For all studied time, the almost optimum concentrations of reducing sugars were obtained at 0. %w of α-amylase (an intermediate level of the enzyme). The highest concentration of reducing sugar was.04 g/l by using 0.7 %w of α-amylase and 40 min.
5 Alpha amylase (%w) Alpha amylase (%w) Fig. 4. Response surface and contour plot of α-amylase amount vs. time (the enzymatic hydrolysis of prebiotic extracted jackfruit seeds at 90 C) 4. CONCLUSIONS A process for the enzymatic hydrolysis step before producing ethanol from prebiotic extracted jackfruit seeds, a waste from the prebiotic extraction process, has been evaluated. A range of temperatures, time and enzyme amount were established to optimize the hydrolysis condition by RSM which could save at experiment times and cost. The recommended enzyme clarification condition was 0.7% enzyme concentration at C for 40 min. 6. REFERENCES [] A. Mukprasirt and K. Sajjaanantakul, "Physicochemical properties of flour and starch from jackfruit seeds (Artocarpus heterophyllus Lam.) compared with modified starches", Int. J. Food Sci. Tech., 004, Vol. 9, pp [] P.V. Aiyer, "Amylases and their applications", Afr. J. Biotech., 005, Vol. 4, pp [] T. Godfrey, R. Jon and W. Stuart, "Industrial Enzymology", Macmillan Publishers, Inc., New York, 996. [4] C.N. Hamelinck, G.V. Hooijdonk, and A.P.C. Faaij, "Ethanol from lignocellulosic biomass: technoeconomic performance in short-, middle- and longterm", Biomass Bioenerg., 005, Vol. 8, pp [5] Y. Sun and J. Cheng, "Hydrolysis of lignocellulosic materials for ethanol production", Bioresource Technol., 00, Vol. 8, pp. -. [6] Z. Konsula and M. Liakopoulou-Kyriakides, "Hydrolysis of starches by the action of an [alpha]- amylase from Bacillus subtilis", Process Biochem., 004, Vol. 9, pp [7] P. Kaur and T. Satyanarayana, "Production of cellbound phytase by Pichia anomala in an economical cane molasses medium: Optimization using statistical tools", Process Biochem., 005, Vol. 40, pp [8] V.V.R. Bandaru, S.R. Somalanka, D.R. Mendu, N.R. Madicherla, and A. Chityala, "Optimization of fermentation conditions for the production of ethanol from sago starch by co-immobilized amyloglucosidase and cells of Zymomonas mobilis using response surface methodology", Enzyme Microb. Tech., 006, Vol. 8, pp [9] Q. Wang, H. Ma, W. u, L. Gong, W. Zhang and D. Zou, "Ethanol production from kitchen garbage using response surface methodology", Biochem. Eng. J., 008, Vol. 9, pp. 4-. [0] L.V.A. Reddy, Y.J. Wee, J.S. Yun and H.W. Ryu, "Optimization of alkaline protease production by batch culture of Bacillus sp. RKY through Plackett- Burman and response surface methodological approaches", Bioresource Technol., 008, Vol. 99, pp [] S. Teerapatr, K. Lerdluk and S. La-aied, "Approach of Cassava Waste Pretreatments for Fuel Ethanol Production in Thailand", Chulalongkorn J. Sci. Res., 006, Vol.. [] G.L. Miller, "Use of dinitrosalicyclic acid reagent for determination of reducing sugar", Anal. Chem., 959, Vol., pp ACKNOWLEDGEMENTS The author gratefully acknowledges the financial support from the Graduate School of Prince of Songkla University, DoE (Discipline of Excellent chemical engineering) and NRCT 0.
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