Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Federal de Goiás (UFG)-
Autor(es): dc.contributorAvenida Senador Salgado Filho-
Autor(es): dc.contributorUniversity of Central Lancashire-
Autor(es): dc.creatorLeite, Daniel Carvalho [UNESP]-
Autor(es): dc.creatorCorrêa, Aretha Arcenio Pimentel [UNESP]-
Autor(es): dc.creatorCunha Júnior, Luis Carlos-
Autor(es): dc.creatorLima, Kássio Michell Gomes de-
Autor(es): dc.creatorMorais, Camilo de Lelis Medeiros de-
Autor(es): dc.creatorVianna, Viviane Formice [UNESP]-
Autor(es): dc.creatorTeixeira, Gustavo Henrique de Almeida-
Autor(es): dc.creatorDi Mauro, Antonio Orlando [UNESP]-
Autor(es): dc.creatorUnêda-Trevisoli, Sandra Helena [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:25:48Z-
Data de disponibilização: dc.date.available2022-02-22T00:25:48Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.jfca.2020.103536-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/198879-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/198879-
Descrição: dc.descriptionIn soybean (Glycine max L.) breeding programs, segregation is normally observed, and it is not possible to have replicates of individuals because each genotype is a unique copy. Therefore, near-infrared spectroscopy (NIRS) was used as a non-destructive tool to classify soybeans by genotypes and to predict oil content. A total of 260 soybean genotypes were divided into five classes, which were composed of 32, 52, 82, 46, and 49 samples of the BV, BVV, EB, JAB, and L class, respectively. NIR spectra were obtained using oven-dried samples (80 g) in a reflectance mode. A successive projection algorithm and genetic algorithm with linear discriminant analysis discriminated genotypes of the low (L class) from the high (EB class) for oil content (88.89% accuracy). The partial least square regression models for oil content were considered good (root mean square error of prediction of 0.96%). Therefore, NIRS can be used as a non-destructive tool in soybean breeding programs, but further investigation is necessary to improve the robustness of the models. It is important to note that to use the models, it is necessary to collect NIR spectra from dry soybean samples.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionUniversidade Estadual Paulista (UNESP) Faculdade de Ciências Agrárias e Veterinárias (FCAV) Campus de Jaboticabal, Via deacesso Prof. Paulo Donato Castellane s/n-
Descrição: dc.descriptionUniversidade Federal de Goiás (UFG) Escola de Agronomia (EA) Goânia – GO, Rodovia Goiânia/Nova Veneza Km 0 Campos Samambaia-
Descrição: dc.descriptionUniversidade Federal do Rio Grande do Norte (UFRN) Instituto de Química Química Biológica e Quimiometria Avenida Senador Salgado Filho, n° 3000, Bairro de Lagoa Nova-
Descrição: dc.descriptionSchool of Pharmacy and Biomedical Sciences University of Central Lancashire, Preston-
Descrição: dc.descriptionUniversidade Estadual Paulista (UNESP) Faculdade de Ciências Agrárias e Veterinárias (FCAV) Campus de Jaboticabal, Via deacesso Prof. Paulo Donato Castellane s/n-
Descrição: dc.descriptionFAPESP: 2011/12958-9-
Idioma: dc.languageen-
Relação: dc.relationJournal of Food Composition and Analysis-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectGenetic algorithm (GA) with LDA (GA-LDA)-
Palavras-chave: dc.subjectGlycine maxL.-
Palavras-chave: dc.subjectPCA with linear discriminant analysis (PCA-LDA)-
Palavras-chave: dc.subjectPrincipal component analysis (PCA)-
Palavras-chave: dc.subjectSuccessive projection algorithm (SPA) with LDA (SPA-LDA)-
Título: dc.titleNon-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.