Artificial neural network modeling and statistical optimization of medium components to enhance production of exopolysaccharide by Bacillus sp. EPS003

Abstract

Microbial Exopolysaccharides (EPS) have a wide range of applications in food, cosmetics, agriculture, pharmaceutical industries, and environmental bioremediation. The present study aims at enhancing the production of EPS from a soil-isolate Bacillus sp. EPS003. Effects of carbon and nitrogen sources and process conditions were evaluated one factor at a time. Box-Behnken design has been used and a 2.5-fold increase in yield is reported after optimizing the most influential parameters sucrose, yeast extract, and peptone as identified by the Plackett-Burman method. An artificial neural network (ANN) with two different topologies (EPS-NN1 and EPS-NN2) was developed. On comparing prediction accuracy, EPS-NN2 formulated as one input layer with four input variables (sucrose, yeast extract, peptone, biomass), a single hidden layer with seven neurons and EPS yield in the output layer showed a high coefficient of determination (R²—0.98) and low error (NRMSE—0.024). This study concludes that the consideration of biomass value has increased the prediction accuracy of the model.

Keywords:

• Artificial neural networks
• exopolysaccharide
• optimization
• response surface methodology