工业微生物2013，Vol.43Issue(1)：64-68,5.DOI:10.3969/j.issn.1001-6678.2013.01.012
基于BP神经网络和遗传算法的面包酵母高密度发酵培养基优化
Optimization of baker's yeast high density fermentation medium by optimized BP neural network based on genetic algorithm
谈亚丽 1李啸 2邹嫚 3张江 3姚娟 2李知洪 3俞学锋3
作者信息
- 2. 三峡大学化学与生命科学学院,宜昌443003
- 3. 安琪酵母股份有限公司,宜昌443003
- 折叠
摘要
Abstract
In order to fulfill the high density cultivation of baker's yeast, the back-propagation neural network was adopted to construct a nonlinear predictable model which suggested the relationship between the key factors of the culture medium and the biomass of bakers yeast. And then the global optimization on this model with the genetic algorithm was conducted. Finally the optimal dose of these significant factors was obtained; glucose 52.3 g/L, yeast extract powder 10.4 g/L, (NH4)2SO41. 9 g/L. Using this optimal medium, the biomass of the bakers yeast cultivated in shake flasks was as high as 3. 95108/mL, increased by 61. 2% compared with that of the primitive culture medium. It demonstrated that the application of artificial neural network in the optimization of microbiological culture media was feasible and efficient.关键词
面包酵母/高密度培养/BP神经网络/遗传算法/发酵优化Key words
baker' s yeast/ high density cultivation/ BP neural network/ genetic algorithm/ fermentation optimization引用本文复制引用
谈亚丽,李啸,邹嫚,张江,姚娟,李知洪,俞学锋..基于BP神经网络和遗传算法的面包酵母高密度发酵培养基优化[J].工业微生物,2013,43(1):64-68,5.
