Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2490
Title: Modeling of polygalacturonase enzyme activity and biomass production by aspergillus sojae ATCC 20235
Authors: Tokatlı, Figen
Tarı, Canan
Ünlütürk, Mehmet
Göğüş, Nihan
Keywords: Artificial intelligence
Cross-validation
Filamentous fungi
Polygalacturonase production
Submerged culture
Issue Date: Sep-2009
Publisher: Springer Verlag
Source: Tokatlı, F., Tarı, C., Ünlütürk, M., and Göğüş, N. (2009). Modeling of polygalacturonase enzyme activity and biomass production by aspergillus sojae ATCC 20235. Journal of Industrial Microbiology and Biotechnology, 36(9), 1139-1148. doi:10.1007/s10295-009-0595-y
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.
URI: http://dx.doi.org/10.1007/s10295-009-0595-y
http://hdl.handle.net/11147/2490
ISSN: 1367-5435
1367-5435
Appears in Collections:Food Engineering / Gıda Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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