【摘要】 为提高伊枯草菌素A(Iturin A)的产量,选择发酵培养基中对Iturin A合成有影响的5个组分作为自变量、以Iturin A产量为响应值,设计5因素10水平的均匀设计试验。以均匀设计试验数据为基础,分别建立二次多项式模型和一种改进型人工神经网络模型来优化发酵培养基,最后通过比较两种模型的优劣选择改进型人工神经网络模型优化培养基组分。结果表明,相比于二次多项式模型,基于相同试验设计的改进型人工神经网路模型有更好的拟合精度和泛化能力,使用人工神经网络模型优化后的培养基发酵48 h后,Iturin A产量为1.121(±0.089)g/L,比二次多项式模型优化的培养基高出13.23%,此时Iturin A发酵培养基的优化组分为葡萄糖、KH2PO4、Mg SO·7H4 2O、酵母膏和大豆蛋白胨总氮浓度分别为42.6、3.62、3.14、0.12、2 g/L。更多还原

【Abstract】 In order to improve the yield of Iturin A,five components of fermentation culture medium influencing the synthesis of Iturin A were chosen as independent variables,the yield of Iturin A was used as response value,a uniform design method with 5 factors and 10 levels was designed. Based on the uniform design,a quadratic polynomial model and an improved artificial neural network model were carried out to optimize the culture medium. By comparing the effects of two models,we chose the optimal components of fermentation culture medium predicted by the improved artificial neural network. The results showed that the improved artificial neural network had better fitting precision and generalization capacities than quadratic polynomial model based on the same experimental design. By this improved artificial neural network,the yield of Iturin A reached 1.121(±0.089)g/L after 48 hours of fermentation when the concentrations of glucose,KH2PO4,Mg SO4·7H2O,yeast extract and total nitrogen in soy peptone were 42.6,3.62,3.14,0.12 and 2 g/L,respectively. The yield increased by 13.23%,compared with the yield optimized by quadratic polynomial model.更多还原