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

Author(s)

Leite, Daniel Carvalho ^[1] ; Pimentel Correa, Aretha Arcenio ^{[1, 2]} ; Cunha Junior, Luis Carlos ^[3] ; Gomes de Lima, Kassio Michell ^[4] ; de Morais, Camilo de Lelis Medeiros ^[5] ; Vianna, Viviane Formice ^{[1, 2]} ; de Almeida Teixeira, Gustavo Henrique ^[4] ; Di Mauro, Antonio Orlando ^{[1, 2]} ; Uneda-Trevisoli, Sandra Helena ^{[1, 2]}

Affiliation

^[1] Univ Estadual Paulista UNESP, Fac Ciencias Agr & Vet FCAV, Campus Jaboticabal, BR-14870900 Jaboticabal, SP - Brazil

^[2] de Morais, Camilo de Lelis Medeiros, Univ Cent Lancashire, Sch Pharm \& Biomed Sci, Preston PR1 2HE, Lancs, England.Leite, Daniel Carvalho, Univ Estadual Paulista UNESP, Fac Ciencias Agr & Vet FCAV, Campus Jaboticabal, BR-14870900 Jaboticabal, SP - Brazil

^[3] Univ Fed Goias UFG, Escola Agron EA, Rodovia Goiania, Nova Veneza Km 0 Campos Samambaia, BR-74001970 Goiania, Go - Brazil

^[4] Univ Fed Rio Grande Norte UFRN, Inst Quim Quim Biol & Quimiometria, Ave Senador Salgado Filho 3000, BR-59078970 Natal, RN - Brazil

^[5] Univ Cent Lancashire, Sch Pharm & Biomed Sci, Preston PR1 2HE, Lancs - England

• Document type: Journal article
• Source: Journal of Food Composition and Analysis; v. 91, AUG 2020.
• Web of Science Citations: 0

Abstract

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. (AU)

• FAPESP's process: 11/12958-9 - Mapping QTL's and obtaining early soybean genotypes with high oil content in order crop reform sugar cane areas
• Grantee: Sandra Helena Unêda-Trevisoli
• Support Opportunities: Regular Research Grants