Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2490
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dc.contributor.authorTokatlı, Figen-
dc.contributor.authorTarı, Canan-
dc.contributor.authorÜnlütürk, Mehmet-
dc.contributor.authorGöğüş, Nihan-
dc.date.accessioned2016-11-22T08:44:25Z
dc.date.available2016-11-22T08:44:25Z
dc.date.issued2009-09
dc.identifier.citationTokatlı, 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-yen_US
dc.identifier.issn1367-5435
dc.identifier.issn1367-5435-
dc.identifier.urihttp://dx.doi.org/10.1007/s10295-009-0595-y
dc.identifier.urihttp://hdl.handle.net/11147/2490
dc.description.abstractAspergillus 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.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofJournal of Industrial Microbiology and Biotechnologyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectCross-validationen_US
dc.subjectFilamentous fungien_US
dc.subjectPolygalacturonase productionen_US
dc.subjectSubmerged cultureen_US
dc.titleModeling of polygalacturonase enzyme activity and biomass production by aspergillus sojae ATCC 20235en_US
dc.typeArticleen_US
dc.authoridTR44047en_US
dc.authoridTR130613en_US
dc.institutionauthorTokatlı, Figen-
dc.institutionauthorTarı, Canan-
dc.institutionauthorGöğüş, Nihan-
dc.departmentIzmir Institute of Technology. Food Engineeringen_US
dc.identifier.volume36en_US
dc.identifier.issue9en_US
dc.identifier.startpage1139en_US
dc.identifier.endpage1148en_US
dc.identifier.wosWOS:000269193600002en_US
dc.identifier.scopus2-s2.0-69249220246en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s10295-009-0595-y-
dc.identifier.pmid19479289en_US
dc.relation.doi10.1007/s10295-009-0595-yen_US
dc.coverage.doi10.1007/s10295-009-0595-yen_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.cerifentitytypePublications-
crisitem.author.dept03.08. Department of Food Engineering-
Appears in Collections:Food Engineering / Gıda Mühendisliği
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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