人工神经网络技术用于酵母同化无机硒作用的研究

本文研究了啤酒酵母和假丝酵母对无机硒的同化能力，结果表明假丝酵母对无机硒的耐受力较强；并且假丝酵母对无机硒的同化能力和菌体产率均高于啤酒酵母．另外还研究了金属元素对假丝酵母同化无机硒及生长的影响，运用神经网络技术，以正交试验法构造训练样本，通过预测与优化，得出培养基中的无机金属元素的最佳含量．

A STUDY ON ASSIMILATION OF INORGANIC SELENIUM WITH YEAST USING ARTIFICIAL NEURAL NETWORK

The ability of assimilation of the two species of yeast was studied and compared with. As a result, Candida is better than brewer yeast in enduring the toxicity of inorganic selenium. The population growth of Candida is higher than that of brewer yeast. Meanwhile, the effect of metal elements on the assimilation of inorganic selenium of yeast was studied by using Orthogonal Experimental Method to establish training sample and using Artificial Neural Network to predict and optimize the results. Finally, the optimal combination of inorganic metals in growth medium was obtained.