NDLI: Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235

Content Provider	Springer Nature Link
Author	Tokatli, Figen Tari, Canan Unluturk, S. Mehmet Gogus Baysal, Nihan
Copyright Year	2009
Abstract	Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R 2 of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 value of 0.83, whereas the regression models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 of 0.69.
Starting Page	1139
Ending Page	1148
Page Count	10
File Format	PDF
ISSN	13675435
Journal	Journal of Industrial Microbiology and Biotechnology
Volume Number	36
Issue Number	9
e-ISSN	14765535
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-05-29
Publisher Place	Berlin, Heidelberg
Access Restriction	Subscribed
Subject Keyword	Artificial intelligence Cross-validation Filamentous fungi Polygalacturonase production Submerged culture Microbiology Bioinformatics Biotechnology Genetic Engineering Inorganic Chemistry Biochemistry
Content Type	Text
Resource Type	Article
Subject	Medicine Bioengineering Applied Microbiology and Biotechnology Biotechnology

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