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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/40658
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dc.creatorCésar Fernandesaquinopt_BR
dc.creatorLuiz Carlos Chamhum Salomãopt_BR
dc.creatorAlcinei Mistico Azevedopt_BR
dc.date.accessioned2022-03-31T12:35:42Z-
dc.date.available2022-03-31T12:35:42Z-
dc.date.issued2016-
dc.citation.volume75pt_BR
dc.citation.issue3pt_BR
dc.citation.spage268pt_BR
dc.citation.epage274pt_BR
dc.identifier.doihttps://doi.org/10.1590/1678-4499.467pt_BR
dc.identifier.issn16784499pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/40658-
dc.description.resumoBanana is one of the most consumed fruits in Brazil and an important source of minerals, vitamins and carbohydrates for human diet. The characterization of banana superior genotypes allows identifying those with nutritional quality for cultivation and to integrate genetic improvement programs. However, identification and quantification of the provitamin carotenoids are hampered by the instruments and reagents cost for chemical analyzes, and it may become unworkable if the number of samples to be analyzed is high. Thus, the objective was to verify the potential of indirect phenotyping of the vitamin A content in banana through artificial neural networks (ANNs) using colorimetric data. Fifteen banana cultivars with four replications were evaluated, totaling 60 samples. For each sample, colorimetric data were obtained and the vitamin A content was estimated in the ripe banana pulp. For the prediction of the vitamin A content by colorimetric data, multilayer perceptron ANNs were used. Ten network architectures were tested with a single hidden layer. The network selected by the best fit (least mean square error) had four neurons in the hidden layer, enabling high efficiency in prediction of vitamin A (r2 = 0.98). The colorimetric parameters a* and Hue angle were the most important in this study. High-scale indirect phenotyping of vitamin A by ANNs on banana pulp is possible and feasible.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofBragantiapt_BR
dc.rightsAcesso Abertopt_BR
dc.subject.otherBananapt_BR
dc.subject.otherAnalise colorimétricapt_BR
dc.subject.otherInteligência artificialpt_BR
dc.subject.otherPerceptronspt_BR
dc.subject.otherFenótipopt_BR
dc.titleHigh-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric datapt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.scielo.br/j/brag/a/Jhkxs3Rkxq9WCs5CcL3Mrfd/?format=pdf&lang=enpt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-5196-0851pt_BR
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