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

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 nondestructive 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.

IPPS 2022 - Plant Phenotyping for a Sustainable Future

“…The near infrared spectroscopy (NIRS) is a very efficient method that has been widely used for high-throughput determine various chemical and biochemical structures of agricultural crop (Washburn et al, 2013). For instances, NIRS has been used for high-throughput predicting fiber and nutrient content of dryland cereal cultivars (Brenna and Berardo, 2004;Stubbs et al, 2010), phenotyping of moisture and amylose content in maize (Wang et al, 2019;Dong et al, 2021), evaluating the composition of carbohydrates in soybean (Leite et al, 2020;Singh et al, 2021), detecting biomass of plant root mixtures (Roumet et al, 2006), analyzing available P contents in soils to aid fertilization (Patzold et al, 2020), as well as determining the internal quality and physiological maturity in the fruit (Cunha et al, 2016;de Carvalho et al, 2019;Minas et al, 2021). In our previous studies, the NIRS has been successfully applied for stalk quality determination (Wang et al, 2021), cell wall features and lignocellulose digestibility characterization in sugarcane Adnan et al, 2022).…”

A high-throughput phenotyping assay for precisely determining stalk crushing strength in large-scale sugarcane germplasm

“…The near infrared spectroscopy (NIRS) is a very efficient method that has been widely used for high-throughput determine various chemical and biochemical structures of agricultural crop ( Washburn et al., 2013 ). For instances, NIRS has been used for high-throughput predicting fiber and nutrient content of dryland cereal cultivars ( Brenna and Berardo, 2004 ; Stubbs et al., 2010 ), phenotyping of moisture and amylose content in maize ( Wang et al., 2019 ; Dong et al., 2021 ), evaluating the composition of carbohydrates in soybean ( Leite et al., 2020 ; Singh et al., 2021 ), detecting biomass of plant root mixtures ( Roumet et al., 2006 ), analyzing available P contents in soils to aid fertilization ( Patzold et al., 2020 ), as well as determining the internal quality and physiological maturity in the fruit ( Cunha et al., 2016 ; de Carvalho et al., 2019 ; Minas et al., 2021 ). In our previous studies, the NIRS has been successfully applied for stalk quality determination ( Wang et al., 2021 ), cell wall features and lignocellulose digestibility characterization in sugarcane ( Li et al., 2021 ; Adnan et al., 2022 ).…”