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

Página do item simplificado
dc.creatorCésar Fernandesaquino
dc.creatorLuiz Carlos Chamhum Salomão
dc.creatorAlcinei Mistico Azevedo
dc.date.accessioned2022-03-31T12:35:42Z
dc.date.accessioned2025-09-09T00:11:19Z
dc.date.available2022-03-31T12:35:42Z
dc.date.issued2016
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttps://doi.org/10.1590/1678-4499.467
dc.identifier.issn16784499
dc.identifier.urihttps://hdl.handle.net/1843/40658
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofBragantia
dc.rightsAcesso Aberto
dc.subjectBanana
dc.subjectAnalise colorimétrica
dc.subjectInteligência artificial
dc.subjectPerceptrons
dc.subjectFenótipo
dc.titleHigh-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data
dc.typeArtigo de periódico
local.citation.epage274
local.citation.issue3
local.citation.spage268
local.citation.volume75
local.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.
local.identifier.orcidhttps://orcid.org/0000-0001-5196-0851
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.scielo.br/j/brag/a/Jhkxs3Rkxq9WCs5CcL3Mrfd/?format=pdf&lang=en

Arquivos

Pacote original

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
High-efficiency phenotyping for vitamin a in banana using artificial neural networks and colorimetric data.pdf
Tamanho:
276.21 KB
Formato:
Adobe Portable Document Format
Baixar

Licença do pacote

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
License.txt
Tamanho:
1.99 KB
Formato:
Plain Text
Descrição:
Baixar

Coleções

Artigo de Periódico

Logo UFMG