High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data

Detalhes bibliográficos
Autor(a) principal: César Fernandesaquino
Data de Publicação: 2016
Outros Autores: Luiz Carlos Chamhum Salomão, Alcinei Mistico Azevedo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: https://doi.org/10.1590/1678-4499.467
http://hdl.handle.net/1843/40658
https://orcid.org/0000-0001-5196-0851
Resumo: CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
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spelling 2022-03-31T12:35:42Z2022-03-31T12:35:42Z2016753268274https://doi.org/10.1590/1678-4499.46716784499http://hdl.handle.net/1843/40658https://orcid.org/0000-0001-5196-0851CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorBanana 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.engUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASBragantiaBananaAnalise colorimétricaInteligência artificialPerceptronsFenótipoHigh-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric datainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.scielo.br/j/brag/a/Jhkxs3Rkxq9WCs5CcL3Mrfd/?format=pdf&lang=enCésar FernandesaquinoLuiz Carlos Chamhum SalomãoAlcinei Mistico Azevedoinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/40658/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALHigh-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data.pdfHigh-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data.pdfapplication/pdf282837https://repositorio.ufmg.br/bitstream/1843/40658/2/High-efficiency%20phenotyping%20for%20vitamin%20a%20in%20banana%20using%20artificial%20neural%20networks%20and%20colorimetric%20data.pdfac34d71ecf725595f24df1e126c0d7feMD521843/406582022-03-31 09:35:42.607oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-03-31T12:35:42Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
title High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
spellingShingle High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
César Fernandesaquino
Banana
Analise colorimétrica
Inteligência artificial
Perceptrons
Fenótipo
title_short High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
title_full High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
title_fullStr High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
title_full_unstemmed High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
title_sort High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
author César Fernandesaquino
author_facet César Fernandesaquino
Luiz Carlos Chamhum Salomão
Alcinei Mistico Azevedo
author_role author
author2 Luiz Carlos Chamhum Salomão
Alcinei Mistico Azevedo
author2_role author
author
dc.contributor.author.fl_str_mv César Fernandesaquino
Luiz Carlos Chamhum Salomão
Alcinei Mistico Azevedo
dc.subject.other.pt_BR.fl_str_mv Banana
Analise colorimétrica
Inteligência artificial
Perceptrons
Fenótipo
topic Banana
Analise colorimétrica
Inteligência artificial
Perceptrons
Fenótipo
description CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
publishDate 2016
dc.date.issued.fl_str_mv 2016
dc.date.accessioned.fl_str_mv 2022-03-31T12:35:42Z
dc.date.available.fl_str_mv 2022-03-31T12:35:42Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1843/40658
dc.identifier.doi.pt_BR.fl_str_mv https://doi.org/10.1590/1678-4499.467
dc.identifier.issn.pt_BR.fl_str_mv 16784499
dc.identifier.orcid.pt_BR.fl_str_mv https://orcid.org/0000-0001-5196-0851
url https://doi.org/10.1590/1678-4499.467
http://hdl.handle.net/1843/40658
https://orcid.org/0000-0001-5196-0851
identifier_str_mv 16784499
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Bragantia
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
bitstream.url.fl_str_mv https://repositorio.ufmg.br/bitstream/1843/40658/1/License.txt
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