Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network
Document title: Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network
Journal: Brazilian archives of biology and technology
Database: PERIÓDICA
System number: 000392641
ISSN: 1516-8913
Authors: 1
1
1
1
2
Institutions: 1Indian Institute of Technology, Microbial Biotechnology and Downstream Processing Laboratory, Kharagpur. India
2National Dairy Research Institute, Karnal, Haryana. India
Year:
Season: Nov-Dic
Volumen: 57
Number: 6
Pages: 962-970
Country: Brasil
Language: Inglés
Document type: Nota breve o noticia
Approach: Experimental
English abstract Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperat ure and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN ). Kinetic growth models were fitted for the cultiva tions using a Fractional Factorial (FF) design experiments for different variables. This novel concept of combining the optimization and modeling presented different optimal conditions for the mixture of B. bifidum and L. acidophilus growth from their one variable at-a-time (OVAT) optimization study. Through these statistical tools, the product yield (cell mass) of the mixture of B. bifidum and L. acidophilus was increased. Regression coefficients (R 2 ) 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 0.08 and 0.3%, respectively. The optimum conditions for the maximum biomass yield were at temperature 38 ° C, pH 6.5, inoculum volume 1.60 mL, inoculum age 30 h, carbon content 42.31% (w/v), and nitrogen content 14.20% (w/v). The results demonstra ted a higher prediction accuracy of ANN compared to RSM
Disciplines: Química,
Biología
Keyword: Química de alimentos,
Bacterias,
Biología celular,
Probióticos,
Lácteos,
Bifidobacterium bifidum,
Lactobacillus acidophilus
Keyword: Chemistry,
Biology,
Food chemistry,
Bacteria,
Cell biology,
Probiotics,
Dairy products,
Bifidobacterium bifidum,
Lactobacillus acidophilus
Full text: Texto completo (Ver PDF)