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.

摘要

微生物胞外多糖 (EPS) 在食品、化妆品、农业、制药工业和环境生物修复等领域有着广泛的应用。本研究旨在提高土壤分离芽孢杆菌EPS 的产量sp。EPS003。碳源和氮源以及工艺条件的影响一次只评估一个因素。已使用 Box-Behnken 设计，据报道，在优化 Plackett-Burman 方法确定的最具影响力的参数蔗糖、酵母提取物和蛋白胨后，产量增加了 2.5 倍。开发了具有两种不同拓扑结构（EPS-NN1 和 EPS-NN2）的人工神经网络 (ANN)。在比较预测精度时，EPS-NN2 制定为具有四个输入变量（蔗糖、酵母提取物、蛋白胨、生物量）的一个输入层，具有七个神经元的单个隐藏层和输出层的 EPS 产量显示出高决定系数（R ^2个—0.98) 和低误差 (NRMSE—0.024)。本研究得出结论，生物量值的考虑提高了模型的预测精度。