Role of Feed Forward Neural Networks Coupled with Genetic Algorithm in Capitalizing of Intracellular Alpha-Galactosidase Production by Acinetobacter sp.

被引：5

作者：

Edupuganti, Sirisha ^{[1
]}

Potumarthi, Ravichandra ^{[2
]}

Sathish, Thadikamala ^{[3
]}

Mangamoori, Lakshmi Narasu ^{[1
]}

机构：

[1] Jawaharlal Nehru Technol Univ Hyderabad, Ctr Biotechnol, Inst Sci & Technol, Hyderabad 500085, Andhra Pradesh, India

[2] Monash Univ, Dept Chem Engn, Clayton, Vic 3800, Australia

[3] Indian Inst Chem Technol, Bioengn & Environm Ctr, Hyderabad 500607, Andhra Pradesh, India

来源：

BIOMED RESEARCH INTERNATIONAL | 2014年 / 2014卷

关键词：

RESPONSE-SURFACE METHODOLOGY; ALKALINE PROTEASE PRODUCTION; SOLID-STATE FERMENTATION; STREPTOMYCES-GRISEOLOALBUS; OPTIMIZATION; ENZYME; IMMOBILIZATION;

D O I：

10.1155/2014/361732

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Alpha-galactosidase production in submerged fermentation by Acinetobacter sp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-2 -value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL.

引用

页数：8

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