基于前反馈神经网络分析优化Saccharomyces cerevisiae L9富硒条件

Optimization of selenium enrichment conditions of Saccharomyces cerevisiae L9 based on pre-feedback neural network analysis

研究以前期分离的一个株富硒能力较强的Saccharomyces cerevisiae L9为材料,利用正交设计与前反馈神经网络结合遗传算法优化其富硒能力。优化结果为:葡萄糖2%、复合氮源为硫酸铵0.35%和蛋白胨1.65%、pH为5.4、接种量为5%、装样量为86mL,初始硒质量浓度17μg/mL,温度30℃,转速180r/min,培养时间48小时,富硒量947μg/g。筛选的菌种具有工业化生产潜质,可作为开发富硒葡萄酒的菌种制剂。

In this study,a strain of Saccharomyces cerevisiae L9 with strong selenium-enrichment ability isolated in the previous period was used as the material and its selenium-enrichment ability was optimized by orthogonal design and pre-feedback neural network combined with genetic algorithm. The optimization results are as follows :glucose 2%,the compound nitrogen source is 0.35%,ammonium sulfate and peptone 1.65%,pH is 5.4,the inoculation amount is 5%,the loading amount is 86 mL,the initial selenium concentration is 17 μg/mL,and the temperature is 30℃,The rotation speed was 180 r/min,the incubation time was 48 hours,and the selenium content was 947μg/g.The selected strains can be used as strain preparations for the development of selenium-enriched wine.