应用神经网络和遗传算法优化利福霉素B发酵培养基  

Optimization of the fermentation medium for rifamycin B production by using artificial neural network and genetic algorithms

作　　者：王军峰[1] 杜吉泉[1] 储炬[1] 庄英萍[1] 张嗣良[1] 

机构地区：[1]华东理工大学生物反应器工程国家重点实验室,上海200237

出　　处：《中国抗生素杂志》2006年第5期278-280,308,共4页Chinese Journal of Antibiotics

基　　金：上海市重点学科项目(034319220)。

摘　　要：对培养基进行优化时,会获得大量的实验数据,但这些实验数据往往不能被进一步利用。如果使用一些不同的方法对历史数据进一步挖掘就可能得出额外的规律。本文使用神经网络对历史数据进行处理,得到了利福霉素B培养基成份和发酵效价之间的关系;再以这个关系作为遗传算法的适应度函数,在一个相对大的空间内快速搜索最优解。优化的培养基使摇瓶发酵效价提高了17.9%,说明所建立的模型能够实现培养基优化的目的并具有数据挖掘的功能。During optimization of medium to promote productivity, lots of data was cumulated. If these data could be dealt with some different methods were a new relationship among them will be found. In this paper, artificial neural network（ANN） was used to predict the relationship between the potency of rifamycin B and the fermentation medium minor error. Two optimized fermentation formula for rifamycin B production were obtained by genetic Algorithms（GAs）, using t-test when alpha was 0.25, and the errors between the actual value and predicted value by ANN were small. The rifamycin B production was increased by 17.9% using the optimized media. Results indicated that the procedure proposed in this work has a great potential for practical application.

关 键 词：利福霉素B 培养基优化 人工神经网络 遗传算法 

分 类 号：TQ920.6[轻工技术与工程—发酵工程]