Comparing a polynomial DOE model and an ANN model for enhanced geranyl cinnamate biosynthesis with Novozym® 435 lipase

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Ferrara (UNIFE)-
Autor(es): dc.contributorUniversidade Federal de Santa Catarina (UFSC)-
Autor(es): dc.creatordo Nascimento, João Francisco Cabral-
Autor(es): dc.creatordos Reis, Bianca Dalbem-
Autor(es): dc.creatorde Baptista Neto, Álvaro-
Autor(es): dc.creatorLerin, Lindomar Alberto-
Autor(es): dc.creatorOliveira, José Vladimir de-
Autor(es): dc.creatorde Paula, Ariela Veloso-
Autor(es): dc.creatorRemonatto, Daniela-
Data de aceite: dc.date.accessioned2025-08-21T20:49:36Z-
Data de disponibilização: dc.date.available2025-08-21T20:49:36Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.bcab.2024.103240-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/297272-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/297272-
Descrição: dc.descriptionCentral Composite Rotatable Design (CCRD), a type of factorial design of experiment is among the most traditional methods used for optimizing bioprocesses, but, in recent years artificial neural networks (ANNs) have emerged as a promising approach for data modeling in bioprocesses. A comparative study between CCRD and ANN modeling for data treatment in the optimization of geranyl cinnamate biosynthesis using Novozym® 435 lipase was conducted. The most effective ANN architecture identified for predicting and maximizing the enzymatic synthesis of geranyl cinnamate was a 3-3-1 neurons model utilizing logsig activation function in the hidden layer. The ANN was trained using all available experimental data and demonstrated a strong fit to the experimental data, coefficient of determination near 1 (R2 = 0.9948) and low Sum-squared Error (SSE = 43.06). The polynomial model (CCRD; R2 = 0.9806; SSE = 161.56) indicated the same optimal conditions as the ANN model, predicting a temperature of 90.2 °C, molar ratio of 1:5.68, and enzyme concentration of 18.6% w/w. Comparison of R2 and SSE values due to lack of fit between both models suggests that ANN predictions closely align with experimental conversion values of geranyl cinnamate. Although the polynomial model is feasible to be applied in enzymatic synthesis, it may be less precise in its predictions than ANN models.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Engineering of Bioprocesses and Biotechnology School of Pharmaceutical Sciences São Paulo State University (UNESP), SP-
Descrição: dc.descriptionDepartment of Chemical Pharmaceutical and Agricultural Sciences University of Ferrara (UNIFE)-
Descrição: dc.descriptionDepartment of Chemical and Food Engineering Federal University of Santa Catarina (UFSC), SC-
Descrição: dc.descriptionDepartment of Engineering of Bioprocesses and Biotechnology School of Pharmaceutical Sciences São Paulo State University (UNESP), SP-
Descrição: dc.descriptionFAPESP: 2020/09592-1-
Descrição: dc.descriptionCNPq: 304399/2022-1-
Idioma: dc.languageen-
Relação: dc.relationBiocatalysis and Agricultural Biotechnology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial neural networks-
Palavras-chave: dc.subjectBioprocess-
Palavras-chave: dc.subjectCentral composite rotatable design-
Palavras-chave: dc.subjectGeranyl cinnamate-
Palavras-chave: dc.subjectLipase-
Título: dc.titleComparing a polynomial DOE model and an ANN model for enhanced geranyl cinnamate biosynthesis with Novozym® 435 lipase-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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