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Assessment of artificial neural networks to predict red colorant production by Talaromyces amestolkiae

dc.contributor.authordos Reis, Bianca Dalbem [UNESP]
dc.contributor.authorde Oliveira, Fernanda [UNESP]
dc.contributor.authorSantos-Ebinuma, Valéria C. [UNESP]
dc.contributor.authorFilletti, Érica Regina [UNESP]
dc.contributor.authorde Baptista Neto, Álvaro [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2023-07-29T12:39:23Z
dc.date.available2023-07-29T12:39:23Z
dc.date.issued2023-01-01
dc.description.abstractConsumer choice is typically influenced by color, leading to an increase in the use of artificial colorants by industry. However, several artificial colorants have been banned due to their harmful effects on human health and the environment, leading to increased interest in colorants from natural sources. Natural colorants can be found in plants, insects, and microorganisms. The importance of evaluating the technical and cost feasibility for the production of natural colorants are important factors for the replacement of artificial counterpart. Therefore, it is highly beneficial to predict the productivity of microbial colorants. The use of statistical methods that generate polynomial models through multiple regressions can provide information of interest about a bioprocess. However, modeling and control of biological processes require complex systems models, because they are nonlinear and non-deterministic systems. In this regard, artificial neural networks are suitable for estimating bioprocess variables with systems modeling. In this work, two different strategies were developed to predict the production of red colorants by Talaromyces amestolkiae, namely simulation by artificial neural networks (ANN) and response surface methodology (RSM). The results showed that the colorant concentration predicted by ANN is closer to the experimental data than that predicted by polynomial models fitted by multiple regression. Thus, this work suggests that the use of ANN can identify the initial conditions of the culture parameters that have the greatest influence on colorant production and can be a tool to be employed to improve the production of biotechnological products, such as microbial colorants.en
dc.description.affiliationDepartment of Engineering Bioprocess and Biotechnology School of Pharmaceutical Sciences Universidade Estadual Paulista, Rodovia Araraquara- Jau Km. 01, SP
dc.description.affiliationDepartment of Engineering Physics and Mathematics Institute of Chemistry Universidade Estadual Paulista, Rodovia Araraquara- Jau Km. 01, SP
dc.description.affiliationDepartment of Biotechnology Lorena School of Engineering University of São Paulo, SP
dc.description.affiliationUnespDepartment of Engineering Bioprocess and Biotechnology School of Pharmaceutical Sciences Universidade Estadual Paulista, Rodovia Araraquara- Jau Km. 01, SP
dc.description.affiliationUnespDepartment of Engineering Physics and Mathematics Institute of Chemistry Universidade Estadual Paulista, Rodovia Araraquara- Jau Km. 01, SP
dc.description.sponsorshipUniversidade Estadual Paulista
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2014/01580-3
dc.description.sponsorshipIdFAPESP: 2019/15493-9
dc.description.sponsorshipIdFAPESP: 2021/06686-8
dc.description.sponsorshipIdFAPESP: 2021/09175-4
dc.description.sponsorshipIdCNPq: 312463/2021-9
dc.format.extent147-156
dc.identifierhttp://dx.doi.org/10.1007/s00449-022-02819-4
dc.identifier.citationBioprocess and Biosystems Engineering, v. 46, n. 1, p. 147-156, 2023.
dc.identifier.doi10.1007/s00449-022-02819-4
dc.identifier.issn1615-7605
dc.identifier.issn1615-7591
dc.identifier.scopus2-s2.0-85142735058
dc.identifier.urihttp://hdl.handle.net/11449/246381
dc.language.isoeng
dc.relation.ispartofBioprocess and Biosystems Engineering
dc.sourceScopus
dc.subjectArtificial neural networks
dc.subjectFilamentous fungi
dc.subjectNatural colorants
dc.subjectTalaromyces amestolkiae
dc.titleAssessment of artificial neural networks to predict red colorant production by Talaromyces amestolkiaeen
dc.typeArtigopt
dspace.entity.typePublication
relation.isOrgUnitOfPublication95697b0b-8977-4af6-88d5-c29c80b5ee92
relation.isOrgUnitOfPublicationbc74a1ce-4c4c-4dad-8378-83962d76c4fd
relation.isOrgUnitOfPublication.latestForDiscovery95697b0b-8977-4af6-88d5-c29c80b5ee92
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Ciências Farmacêuticas, Araraquarapt
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Química, Araraquarapt

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