Computerized study of interactions among factors and their optimization through response surface methodology for the production of tannin acyl hydrolase by Aspergillus niger PKL 104 under solid state fermentation

Optimization of five parameters (initial moisture, initial pH, incubation temperature, inoculum ratio and fermentation period), as per central composite rotable design falling under the response surface methodology, was attempted in a total of 32 experimental sets, after fitting the experimental data to the polynomial model of a suitable degree, for tannin acyl hydrolase production by Aspergillus niger PKL 104 in solid state fermentation system. The quantitative relation between the enzyme production and different levels of these factors was exploited to work out optimized levels of these parameters by flexible polyhedron search method and confirmed by further experimentations. The best set required 5% inoculum, 6.5 initial pH, 28 °C fermentation temperature, 62% initial moisture and 3 days fermentation time. The optima were worked out under the additional constraints for temperature (∼ 30 °C) and fermentation time (not more than 3 days) which are essential from industrial conditions and to pre-empt contamination, respectively. The best set resulted in 1.34 times more enzyme production than that was obtained before this optimization. Three dimensional plots, relating the enzyme production to paired factors (when other three factors were kept at their optimal levels) best described the behaviour of solid state fermentation system and the interactions between factors under optimized conditions. The model showed that the enzyme production was affected by all the five factors studied. The initial pH exhibited a positive interaction with moisture but no interaction with other factors. Initial moisture level and inoculum ratio showed negative interaction in contrast to positive interaction between inoculum ratio and fermentation period. It is thus apparent that the response surface methodology not only gives valuable information on interactions between the factors but also leads to identification of feasible optimum values of the studied factors, in addition to 99% (or more) savings on resources as compared to a full factorial traditional optimization method. Response surface methods have not been used earlier for optimizing parameters in solid state fermentation system.

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