Abstract：
:The acidification behavior of Lactobacillus bulgaricus and Streptococcus thermophilus for yoghurt production was investigated along temperature profiles within the optimal window of 38-44 degrees 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 degrees C. Laboratory evaluation of three replicates of the GA suggested optimum profile of 43, 38, 44, 43, and 39 degrees 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.
journal_name
J Ind Microbiol Biotechnoljournal_title
Journal of industrial microbiology & biotechnologyauthors
Gueguim-Kana EB,Oloke JK,Lateef A,Zebaze-Kana MGdoi
10.1007/s10295-007-0220-xsubject
Has Abstractpub_date
2007-07-01 00:00:00pages
491-6issue
7eissn
1367-5435issn
1476-5535journal_volume
34pub_type
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journal_title：Journal of industrial microbiology & biotechnology
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journal_title：Journal of industrial microbiology & biotechnology
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