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Current Trends in Biotechnology and Pharmacy Vol. 6 (2) 241-254 April 2012, ISSN 0973-8916 (Print), 2230-7303 (Online) Media optimization by Response surface methodology (RSM): The Central composite rotatable design (CCRD) gave quadratic model for the given set of experimental results. Eq. 8 represents the mathematical model relating the production of GSH with the independent process variables and the second order polynomial coefficient for each term of the equation determined through multiple regression analysis using the Design Expert. The coded values, a CCRD matrix of independent variables along with responses of each experimental trial is given in Table 1. The results were analyzed by using 248 ANOVA i.e. analysis of variance suitable for the experimental design used, and cited in Table 2. The ANOVA of the quadratic model indicates that the model is significant. The Model F-value of 270.75 implies the model to be significant and is calculated as ratio of mean square regression and mean square residual. Model P-value (Prob > F) is very low (< 0.0500), again signifying the model to be significant. The P values were used as a tool to check the significance of each of the coefficients, which, in turn are necessary to understand the pattern Table 1. The CCRD matrix of independent variables in coded form and actual values with their corresponding response in terms of production of glutathione by S. cerevisiae NCIM 3454 Sr. No. Glucose (%) Yeast extract (%) MgSO (%) 4 GSH (mg/L) 1 -1 (3.0) -1 (3.0) -1 (1.0) 85.780 ± 0.95 2 1 (9.0) -1 (3.0) -1 (1.0) 48.220 ± 1.02 3 -1 (3.0) 1 (9.0) -1 (1.0) 96.630 ± 1.10 4 1 (9.0) 1 (9.0) -1 (1.0) 110.25 ± 1.21 5 -1 (3.0) -1 (3.0) 1 (2.0) 79.350 ± 0.99 6 1 (9.0) -1 (3.0) 1 (2.0) 69.240 ± 1.41 7 -1 (3.0) 1 (9.0) 1 (2.0) 75.320 ± 1.31 8 1 (9.0) 1 (9.0) 1 (2.0) 105.21 ± 0.82 9 0 (6.0) 0 (6.0) 0 (1.5) 137.63 ± 1.10 10 0 (6.0) 0 (6.0) 0 (1.5) 137.32 ± 1.11 11 0 (6.0) -0 (6.0) 0 (1.5) 138.54 ± 0.71 12 0 (6.0) 0 (6.0) 0 (1.5) 138.24 ± 0.77 13 -1.68 (0.95) 0 (6.0) 0 (1.5) 74.200 ± 1.62 14 1.68 (11.05) 0 (6.0) 0 (1.5) 70.980 ± 0.88 15 0 (6.0) -1.68 (0.95) 0 (1.5) 42.170 ± 0.94 16 0 (6.0) 1.68 (11.50) 0 (1.5) 88.510 ± 0.32 17 0 (6.0) 0 (6.0) -1.68 (0.66) 150.91 ± 1.02 18 0 (6.0) 0 (6.0) 1.68 (2.34) 139.32 ± 0.74 19 0 (6.0) 0 (6.0) 0 (1.5) 137.21 ± 0.83 20 0 (6.0) 0 (6.0) 0 (1.5) 139.77 ± 0.34 a Results are mean ± SD of three determinations Values in the parenthesis indicate the real values of variables Parbatsingh Rajpurohit et al
Current Trends in Biotechnology and Pharmacy Vol. 6 (2) 241-254 April 2012, ISSN 0973-8916 (Print), 2230-7303 (Online) of the mutual interactions between the test variables. The F value and the corresponding P values, along with the coefficient estimate, are given in Table 2. The smaller the magnitude of the P, the more significant is the corresponding coefficient. Values of P less than 0.050 indicate the model terms to be significant. The coefficient estimates and the corresponding P values suggests that, among the test variables used in the study, B, C, A 2 , B 2 , AB, AC and BC (where A = glucose, B = yeast extract, C = MgSO 4 ) are significant model terms. B, A 2 , B 2 and AB (P < 0.0001) have the largest effect on GSH production. Other intearctions were found to be insignificant. The corresponding second-order response model for Eq. (7) that was found after analysis for the regression was: GSH (mg/L) = 139.13 – (0.70 x glucose) + (13.38 x yeast extract) – (2.29 x magnesium sulphate) – (24.94 x glucose 2 ) – (27.50 x yeast extract 2 ) + (0.71 x magnesium sulphate 2 ) + (11.40 x glucose x yeast extract) + (5.47 x glucose x 249 magnesium sulphate) – (5.12 x yeast extract x magnesium sulphate) (7) The fit of the model was also expressed by the coefficient of regression (r 2 ), which was found to be 0.996, indicating that 99.6 % of the confidence level of the model to predict the response (GSH yield). The “Pred R-Squared” of 0.975 is in reasonable agreement with the “Adj R-Squared” of 0.992. “Adeq Precision” measures the signal to noise ratio. A ratio greater than 4 is desirable. Here, the ratio of 47.095 indicates an adequate signal. The special features of the RSM tool, “contour plot generation” and “point prediction” were also studied to find optimum value of the combination of the three media constitutes for the maximum production of GSH. These predicted values were experimentally verified. Table 3 documents the predicted and experimental yields of GSH by various media combination. It was observed that medium containing (%), glucose 5.67, yeast extract, 7.13 and magnesium sulphate, 0.66 yielded maximum (148.45 mg/L) GSH. Table 2. Analysis of variance (ANOVA) for the experimental results of the central composite design (Quadratic Model) Factor a Estimate Sum of Standard DF b F value p Coefficient squares Error Intercept 139.13 61.28 1.25 1 270.75 < 0.0001 A -0.70 6.71 0.82 1 0.73 0.4159 B 13.38 2445.59 0.82 1 264.87 < 0.0001 C -2.29 71.52 0.82 1 7.75 0.0213 A 2 -24.94 8953.57 0.80 1 969.70 < 0.0001 B 2 -27.50 10888.97 0.80 1 1179.31 < 0.0001 C 2 0.71 7.18 0.80 1 0.78 0.4009 AB 11.40 1039.22 1.07 1 112.55 < 0.0001 AC 5.47 238.93 1.07 1 25.88 0.0007 BC -5.12 209.51 1.07 1 22.69 0.0010 a A = glucose, B = yeast extract, C = MgSO4 b Degree of freedom, p < 0.05 (models are significant), R 2 = 0.99 Enhanced Production of Glutathione
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