Author(s):
Leite, Daniel Carvalho [UNESP] ; Corrêa, Aretha Arcenio Pimentel [UNESP] ; Cunha Júnior, Luis Carlos ; Lima, Kássio Michell Gomes de ; Morais, Camilo de Lelis Medeiros de ; Vianna, Viviane Formice [UNESP] ; Teixeira, Gustavo Henrique de Almeida ; Di Mauro, Antonio Orlando [UNESP] ; Unêda-Trevisoli, Sandra Helena [UNESP]
Date: 2020
Persistent ID: http://hdl.handle.net/11449/198879
Origin: Oasisbr
Subject(s): Genetic algorithm (GA) with LDA (GA-LDA); Glycine maxL.; PCA with linear discriminant analysis (PCA-LDA); Principal component analysis (PCA); Successive projection algorithm (SPA) with LDA (SPA-LDA); Genetic algorithm (GA) with LDA (GA-LDA); Genetic algorithm (GA) with LDA (GA-LDA); Glycine maxL.; Glycine maxL.; PCA with linear discriminant analysis (PCA-LDA); PCA with linear discriminant analysis (PCA-LDA); Principal component analysis (PCA); Principal component analysis (PCA); Successive projection algorithm (SPA) with LDA (SPA-LDA); Successive projection algorithm (SPA) with LDA (SPA-LDA)
Description
Made available in DSpace on 2020-12-12T01:24:27Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-08-01
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
In 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.
Universidade Estadual Paulista (UNESP) Faculdade de Ciências Agrárias e Veterinárias (FCAV) Campus de Jaboticabal, Via deacesso Prof. Paulo Donato Castellane s/n
Universidade Federal de Goiás (UFG) Escola de Agronomia (EA) Goânia – GO, Rodovia Goiânia/Nova Veneza Km 0 Campos Samambaia
Universidade Federal do Rio Grande do Norte (UFRN) Instituto de Química Química Biológica e Quimiometria Avenida Senador Salgado Filho, n° 3000, Bairro de Lagoa Nova
School of Pharmacy and Biomedical Sciences University of Central Lancashire, Preston
Universidade Estadual Paulista (UNESP) Faculdade de Ciências Agrárias e Veterinárias (FCAV) Campus de Jaboticabal, Via deacesso Prof. Paulo Donato Castellane s/n
FAPESP: 2011/12958-9
