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المؤلفTaylan, Osman
المؤلفYilmaz, Mustafa Tahsin
المؤلفBalubaid, Mohammed
المؤلفAlamoudi, Rami
المؤلفEl-Obeid, Tahra
المؤلفDertli, Enes
المؤلفŞahin, Engin
المؤلفBakhsh, Ahmed
المؤلفHerrera-Viedma, Enrique
تاريخ الإتاحة2020-02-16T10:02:45Z
تاريخ النشر2020-02-01
اسم المنشورInternational Journal of Ecosystems and Ecology Science (IJEES)
الاقتباسTaylana, Osman et. al. Machine Learning Application for Optimizing Asymmetrical Reduction of Acetophenone Employing Complete Cell of Lactobacillus Senmaizuke as an Environmentally Friendly Approach. International Journal of Ecosystems and Ecology Science (IJEES). Vol. 10 (1), 123-136 (2020)
الرقم المعياري الدولي للكتاب2224-4980
معرّف المصادر الموحدhttp://dx.doi.org/10.31407/ijees10.117
معرّف المصادر الموحدhttp://hdl.handle.net/10576/12895
الملخصRecently, 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 ºC, 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 %.
راعي المشروعKing Abdulaziz university, under grant No. (135 -197 - D1439)
اللغةen
الناشرHysen MANKOLLI, Earth System Science Interdisciplinary Center (ESSIC)
الموضوعSourdough
Asymmetric bioreduction
Biocatalyst
Chirality
Machine learning
ANNs
Biotransformation
العنوانMachine Learning Application for Optimizing Asymmetrical Reduction of Acetophenone Employing Complete Cell of Lactobacillus Senmaizuke as an Environmentally Friendly Approach
النوعArticle
الصفحات123 - 136
رقم العدد1
رقم المجلد10
dc.accessType Full Text


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