外文摘要：This study aimed to enhance the production of mycelium biomass and exopolysaccharides (EPS) of Pleurotus ostreatus 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 degrees 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 2 ) 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.
外文关键词：machine learning;genetic algorithm;Bioactive compounds;Edible fungi;Graphical user interface
作者：Hamza, Arman;Khalad, Abdul;Kumar, Devarai Santhosh
作者单位：Indian Inst Technol Hyderabad;IIT Hyderabad
期刊名称：BIORESOURCE TECHNOLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：399
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台