Application of Central Composite Design and Artificial Neural Network for the Optimization of Fermentation Conditions for Lipase Production by Rhizopus arrhizus MTCC 2233

Abstract

The response surface optimization strategy was used to enhance the lipase production by Rhizopus arrhizus MTCC 2233 in submerged fermentation. Various vegetable oils were experimented as an inducer using the optimized medium to study the influence on lipase production, and corn oil was found to be the best inducer for lipase production by Rhizopus arrhizus. The optimization of fermentation conditions, temperature, initial pH and agitation speed was carried out using corn oil as the inducer. Statistical analysis of the experimental data showed that the temperature, agitation speed, quadratic effects of temperature, initial pH and agitation speed and interactive effects of temperature and agitation speed are significant parameters that affect lipase production. The optimum fermentation conditions were achieved at 32°C; pH 6.0 and agitation speed of 107 rpm with the maximum lipase activity of 4.32 U/mL. Artificial neural network model was used to predict the lipase activity and cell mass production under various fermentation conditions. Unstructured kinetic models, Logistic model, Luedeking-Piret model and modified Luedeking-Piret model were used to describe the cell biomass, lipase production and glucose utilization kinetics respectively.