Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var. oligosporus and different agro-industrial wastes

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dc.contributor.authorFernandez Nunez, Eutimio Gustavo [UNESP]
dc.contributor.authorBarchi, Augusto Cesar [UNESP]
dc.contributor.authorIto, Shuri [UNESP]
dc.contributor.authorEscaramboni, Bruna [UNESP]
dc.contributor.authorHerculano, Rondinelli Donizetti [UNESP]
dc.contributor.authorMalacrida Mayer, Cassia Roberta [UNESP]
dc.contributor.authorNeto, Pedro de Oliva [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T17:21:10Z
dc.date.available2018-11-26T17:21:10Z
dc.date.issued2017-03-01
dc.description.abstractBACKGROUND: Culture medium is a key element to be defined when biotechnologies are chosen for agro-industrial wastes reutilization. This work aimed at definition of culture medium composition using four agro-industrial wastes (wheat bran, type II wheat flour, soybean meal and sugarcane bagasse) in solid-state fermentation (SSF) of Rhizopus oligosporus, for high-level production of amylases through approaches based on artificial intelligence (AI) or response surface methodologies (RSM). First, substrates were individually assessed. Then, I-optimal mixture experimental designs were performed to determine the influence of two sets of ternary agro-industrial waste mixtures on amylase and specific amylase activities. RESULTS: The best individual substrate for amylases production was wheat bran (392.5 U g(-1)). As a rule, no significant interactions among substrates affecting amylase activities were observed for ternary systems and the approaches under consideration. A significant exception was the amylolytic activity for mixtures composed of wheat bran (91% w/w) and soybean meal (9% w/w). This finding was confirmed analytically by a combination of artificial neural network (ANN) and genetic algorithm (GA). The AI approach improved modelling quality with respect to RSM for production of fungal amylases in SSF. CONCLUSION: The I-optimal design in conjunction with ANN-GA is suggested to optimize accurately culture medium to maximize amylase production by SSF. (C) 2016 Society of Chemical Industryen
dc.description.affiliationUniv Estadual Paulista, Dept Ciencias Biol, Grp Engn Bioproc, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, Brazil
dc.description.affiliationUniv Estadual Paulista, Dept Ciencias Biol, Lab Biotecnol Ind, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, Brazil
dc.description.affiliationUniv Estadual Paulista, Dept Ciencias Biol, Lab Fis Biofis & Biomat, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, Brazil
dc.description.affiliationUniv Estadual Paulista, Dept Ciencias Biol, Lab Quim Alimentos & Nanobiotecnol, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Ciencias Biol, Grp Engn Bioproc, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Ciencias Biol, Lab Biotecnol Ind, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Ciencias Biol, Lab Fis Biofis & Biomat, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Ciencias Biol, Lab Quim Alimentos & Nanobiotecnol, Campus Assis,Ave Dom Antonio 2100, BR-19806900 Assis, SP, Brazil
dc.description.sponsorshipFundação para o Desenvolvimento da UNESP (FUNDUNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFUNDUNESP: 0312/001/14-Prope/CDC
dc.description.sponsorshipIdFAPESP: 14/06447-0
dc.format.extent684-692
dc.identifierhttp://dx.doi.org/10.1002/jctb.5054
dc.identifier.citationJournal Of Chemical Technology And Biotechnology. Hoboken: Wiley, v. 92, n. 3, p. 684-692, 2017.
dc.identifier.doi10.1002/jctb.5054
dc.identifier.issn0268-2575
dc.identifier.lattes4638952263502744
dc.identifier.orcid0000-0001-9378-9036
dc.identifier.urihttp://hdl.handle.net/11449/162617
dc.identifier.wosWOS:000397400500026
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofJournal Of Chemical Technology And Biotechnology
dc.relation.ispartofsjr0,766
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectenzymes
dc.subjectexperimental design
dc.subjectindustrial microbiology
dc.subjectmathematical modelling
dc.subjectwaste treatment and waste minimization
dc.subjectsolid state fermentation
dc.titleArtificial intelligence approach for high level production of amylase using Rhizopus microsporus var. oligosporus and different agro-industrial wastesen
dc.typeArtigo
dcterms.licensehttp://olabout.wiley.com/WileyCDA/Section/id-406071.html
dcterms.rightsHolderWiley-Blackwell
unesp.author.lattes4638952263502744[7]
unesp.author.lattes2114367431104728[6]
unesp.author.orcid0000-0002-2800-392X[1]
unesp.author.orcid0000-0002-2285-3624[4]
unesp.author.orcid0000-0001-7236-0847[5]
unesp.author.orcid0000-0001-9378-9036[7]
unesp.author.orcid0000-0003-0069-6581[6]

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