Novel optimal temperature profile for acidification process of Lactobacillus bulgaricus and Streptococcus thermophilus in yoghurt fermentation using artificial neural network and genetic algorithm

• Published in: Journal of industrial microbiology & biotechnology Vol. 34; no. 7; pp. 491 - 496
• Main Authors: Gueguim-Kana, E. B, Oloke, J. K, Lateef, A, Zebaze-Kana, M. G
• Format: Journal Article
• Language: English
• Published: Heidelberg Berlin/Heidelberg : Springer-Verlag 01.07.2007; Springer; Oxford University Press
• Subjects: Acidification; Acids - chemistry; Acids - metabolism; Algorithms; Biological and medical sciences; Biotechnology; Fermentation; food processing; Fundamental and applied biological sciences. Psychology; Genetic algorithms; Hydrogen-Ion Concentration; Industrial Microbiology - methods; Lactobacillus - genetics; Lactobacillus - growth & development; Lactobacillus - metabolism; Lactobacillus bulgaricus; Lactobacillus delbrueckii subsp. bulgaricus; microbial physiology; Mutation; Neural networks; Neural Networks (Computer); nonlinear models; starter cultures; Streptococcus thermophilus; Streptococcus thermophilus - genetics; Streptococcus thermophilus - growth & development; Streptococcus thermophilus - metabolism; Studies; Temperature; Yogurt; Yogurt - microbiology; Lactic acid bacteria; Temperature; Process optimization; Acidification; Yoghurt acidification profile; Yogurt; Neural network; Fermentation; Optimization; Streptococcaceae; Lactobacillus delbrueckii subsp. bulgaricus; Streptococcus salivarius subsp. thermophilus; Genetic algorithm; Bacteria; Lactobacillaceae; Micrococcales; Artificial neural network; Operating process; Temperature profile
• ISSN: 1367-5435; 1476-5535
• DOI: 10.1007/s10295-007-0220-x

The acidification behavior of Lactobacillus bulgaricus and Streptococcus thermophilus for yoghurt production was investigated along temperature profiles within the optimal window of 38-44 °C. For the optimal acidification temperature profile search, an optimization engine module built on a modular artificial neural network (ANN) and genetic algorithm (GA) was used. Fourteen batches of yoghurt fermentations were evaluated using different temperature profiles in order to train and validate the ANN sub-module. The ANN captured the nonlinear relationship between temperature profiles and acidification patterns on training data after 150 epochs. This served as an evaluation function for the GA. The acidification slope of the temperature profile was the performance index. The GA sub-module iteratively evolved better temperature profiles across generations using GA operations. The stopping criterion was met after 11 generations. The optimal profile showed an acidification slope of 0.06117 compared to an initial value of 0.0127 and at a set point sequence of 43, 38, 44, 43, and 39 °C. Laboratory evaluation of three replicates of the GA suggested optimum profile of 43, 38, 44, 43, and 39 °C gave an average slope of 0.04132. The optimization engine used (to be published elsewhere) could effectively search for optimal profiles of different physico-chemical parameters of fermentation processes.