改进型人工神经网络优化 Iturin A
发酵培养基研究

付茂红 1; 3; 钟 娟 1; 2; 3; $2 1; 3; $2 1; 2; 杨 杰 1; 2; $2 1; 2; 谭 红 1; 2

文章摘要

Optimization of fermentation medium for Iturin A byimproved artificial neural network approach

DOI：

Author Name	Affiliation
付茂红 1， 3 ，钟娟 1， 2， 3 ，谭忠元 1， 3 ，彭文璟 1， 2 ，杨杰 1， 2 ，周金燕 1， 2 ，谭红 1， 2	1. 中国科学院环境与应用微生物重点实验室（成都生物研究所），四川成都 610041； 2. 四川省环境微生物重点实验室，四川成都 610041； 3. 中国科学院大学，北京 100049

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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， KH 2 PO 4 ， MgSO 4 •7H 2 O， 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.

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