This study aimed to enhance the production of mycelium biomass and exopolysaccharides (EPS) of in submerged fermentation. Response Surface Methodology (RSM)sought to optimize culture conditions, whereas Artificial Neural Network (ANN)aimed to predict the mycelium biomass and EPS. After optimization of RSM model conditions, the maximum biomass (36.45 g/L) and EPS (6.72 g/L) were obtained at the optimum temperature of 22.9 °C, pH 5.6, and agitation of 138.9 rpm. Further, the Genetic Algorithm (GA) was employed to optimize the cultivation conditions in order to maximize the mycelium biomass and EPS production. The ANN model with an optimized network structure gave the coefficient of determination (R) value of 0.99 and the least mean squared error of 1.9 for the validation set. In the end, a graphical user interface was developed to predict mycelium biomass and EPS production.

中文翻译：

---

响应面法和人工神经网络相结合提高平菇菌丝体生物量和胞外多糖的产量

本研究旨在提高深层发酵中菌丝体生物量和胞外多糖（EPS）的产量。响应面法（RSM）旨在优化培养条件，而人工神经网络（ANN）旨在预测菌丝体生物量和EPS。经过RSM模型条件优化后，在最适温度22.9℃、pH 5.6、搅拌速度138.9rpm下获得最大生物量（36.45g/L）和EPS（6.72g/L）。此外，采用遗传算法（GA）来优化培养条件，以最大限度地提高菌丝体生物量和 EPS 产量。具有优化网络结构的 ANN 模型给出的验证集决定系数 (R) 值为 0.99，最小均方误差为 1.9。最后，开发了一个图形用户界面来预测菌丝体生物量和 EPS 产量。