STATISTICAL OPTIMIZATION OF PROCESS VARIABLES USING CENTRAL COMPOSITE DESIGN FOR ENHANCED L-METHIONINASE PRODUCTION BY ASPERGILLUS FLAVIPES

Response Surface Methodology (RSM) based on 4 factor Central Composite Design (CCD) was employed to obtain the best possible combination of process variables for enhanced L-methioninase production in solid state fermentation (SSF) by Aspergillus flavipes. These variables include inoculum volume, initial moisture content, carbon supplement concentration and pH. The experimental data obtained was fitted to a second-order polynomial equation using multiple regression analysis and analyzed by Analysis of Variance (ANOVA). 3D response surface plots derived from the mathematical models were applied to ascertain the optimal conditions. The model came out to be highly significant and the statistical analysis results showed that the interaction of medium components were strong. The predicted optimum levels were as follows: inoculum volume - 3.0814 ml, initial moisture content - 62.19003% (v/w), carbon supplement concentration - 0.9995% (w/v) and pH - 8.36124. Under these optimum conditions, the experimental yield of L-methioninase was 397 U/gds, which was in close agreement with the value predicted by the model, 396.5811 U/gds. The value of regression coefficient R2 = 0.9874, indicated that 98.74% of the variability in the response could be explained by the model. The enzyme L-methioninase was partially purified using 70% saturated ammonium sulphate followed by dialysis. The purification fold increased from 1 in the crude extract to 2.606 in the dialyzed enzyme sample.