Optimization of aqueous extraction process to enhance the production of phytase by Rhizopus oryzae using response surface methodology coupled with artificial neural network

Abstract

Aqueous extraction process was optimized to reduce endotoxins from mixed substrate (1:1) for further phytase production by Rhizopus oryzae in solid state fermentation. 23 factorial design of experiment was combined with either a back-propagation artificial neural network (ANN) or the response surface methodology (RSM) for optimizing the process variables (length of extraction time, substrate loading and different pH of extraction solvent) to predict and simulate phytase production and phosphorus release. ANN was found to be a more powerful tool than RSM, for modeling and optimizing variables for the aqueous extraction process and can be used for predictive simulations of a process. A 2.37-fold increase in phytase production (37.65 U/gds) was achieved at the model predicted optimum concentration of extraction time of 29.78 min, substrate loading at 11.04 g and pH of extraction solvent at 7.1 as compared to the phytase yield in untreated substrate (15.91 U/gds). Moreover, the reduction in phytic acid after aqueous extraction of substrates was validated after high performance liquid chromatography (HPLC) characterization study. The results suggest that aqueous extraction process can be used efficiently for reducing the endogenous anti-nutritional factors from substrates eventually leading to enhanced phytase yield.

Keywords: Rhizopus oryzae, high performance liquid chromatography (HPLC), phytic acid, solid state fermentation, optimization.