Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN
Braz. arch. biol. technol
;
57(1): 15-22, Jan.-Feb. 2014. ilus, graf, tab
Article
in English
| LILACS
| ID: lil-702564
ABSTRACT
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.
Full text:
Available
Index:
LILACS (Americas)
Type of study:
Prognostic study
Language:
English
Journal:
Braz. arch. biol. technol
Journal subject:
Biology
Year:
2014
Type:
Article
Affiliation country:
India
Institution/Affiliation country:
Indian Institute of Technology/IN
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