Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN

Brazilian Archives of Biology and Technology

Ganga Sahay Meena²  Nitin Kumar²  Gautam Chandra Majumdar²  Rintu Banerjee²  Pankaj Kumar Meena¹  Vijesh Yadav¹ 

[1] ,Indian Institute of Technology Microbial Biotechnology and Downstream Processing Laboratory Department of Agricultural and Food Engineering,Kharagpur

关键词: Response surface methodology (RSM);    Artificial neural network (ANN);    Genetic algorithms (GA);    Box-behnken besign (BBD);   

DOI  :  10.1590/S1516-89132014000100003

来源: SciELO

【 摘 要 】

The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This concept of combining the optimization and modeling presented different optimal conditions for L. acidophilus growth from their original optimization study. Through these statistical tools, the product yield (cell mass) of L. acidophilus was increased. Regression coefficients (R²) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.06 and 0.2%, respectively. The results demonstrated a higher prediction accuracy of ANN compared to RSM.

【 授权许可】

CC BY-NC   
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