MACHINE LEARNING APPLICATION FOR OPTIMIZING ASYMMETRICAL REDUCTION OF ACETOPHENONE EMPLOYING COMPLETE CELL OF LACTOBACILLUS SENMAIZUKE AS AN ENVIRONMENTALLY FRIENDLY APPROACH

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dc.contributor.authorTaylan, Osman
dc.contributor.authorYilmaz, Mustafa Tahsin
dc.contributor.authorBalubaid, Mohammed
dc.contributor.authorAlamoudi, Rami
dc.contributor.authorEl-Obeid, Tahra
dc.contributor.authorDertli, Enes
dc.contributor.authorSahin, Engin
dc.date.accessioned2024-10-04T18:51:22Z
dc.date.available2024-10-04T18:51:22Z
dc.date.issued2020
dc.departmentBayburt Üniversitesien_US
dc.description.abstractRecently, optimization of the bioreduction reactions by optimization methodologies has gained special interest as these reactions are affected by several extrinsic factors that should be optimized for higher yields. An important example for these kinds of reactions is the complete cell implications for the bioreduction of prochiral ketones in which the culture parameters play crucial roles. Such biocatalysts provide environmentally friendly and clean methodology to perform reactions under mild conditions with high conversion rates. In the present work, at the first step the Lactobacillus senmaizuke was isolated from sourdough and the complete cell application of Lactobacillus senmaizuke for the bioreduction of acetophenone was optimized by an Artificial Neural networks (ANNs) to achieve the highest enantiomeric excess (EE, %). The culture parameters, pH, temperature, incubation period and agitation speed were the experimental factors that were optimized to maximize EE (%) by machine learning algorithm of Artificial Intelligence modeling and the best conditions to maximize EE (95.5 %) were calculated to be pH of 5.7, temperature of 35 degrees C, incubation period of 76 h and agitation speed of 240 rpm with very low sum of squared error value (0.611236 %) to bioreduce acetophenone using complete cell of Lactobacillus senmaizuke as a sourdough isolate GRAS microbial species. Accordingly, The ANN was employed to correctly establish the enantiomeric excess values of the specimen with an average absolute error 0.080739 %.en_US
dc.description.sponsorshipDeanship of Scientific Research (DSR), King Abdulaziz University, Jeddah [135 -197 -D1439]; DSRen_US
dc.description.sponsorshipThis work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (135 -197 -D1439). The authors, therefore, gratefully acknowledge the DSR technical and financial support.en_US
dc.identifier.doi10.31407/ijees10.117
dc.identifier.endpage136en_US
dc.identifier.issn2224-4980
dc.identifier.issue1en_US
dc.identifier.startpage123en_US
dc.identifier.urihttps://doi.org/10.31407/ijees10.117
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3473
dc.identifier.volume10en_US
dc.identifier.wosWOS:000523229800017en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherHealth & Environment Assocen_US
dc.relation.ispartofInternational Journal of Ecosystems and Ecology Science-Ijeesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSourdoughen_US
dc.subjectAsymmetric bioreductionen_US
dc.subjectBiocatalysten_US
dc.subjectChiralityen_US
dc.subjectMachine learningen_US
dc.subjectANNsen_US
dc.subjectBiotransformationen_US
dc.titleMACHINE LEARNING APPLICATION FOR OPTIMIZING ASYMMETRICAL REDUCTION OF ACETOPHENONE EMPLOYING COMPLETE CELL OF LACTOBACILLUS SENMAIZUKE AS AN ENVIRONMENTALLY FRIENDLY APPROACHen_US
dc.typeArticleen_US

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