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Updated: Jul 18, 2026

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis
Published on: April 11, 2016
Artificial neural network modeling and statistical optimization of medium components to enhance production of
Sivasankari Marimuthu1, Sharon Mano Pappu J2,3, Karthikeyan Rajendran1
1Department of Biotechnology, Mepco Schlenk Engineering College, Sivakasi, India.
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.
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