Vis/NIR hyperspectral imaging distinguishes sub-population, production environment, and physicochemical grain properties in rice

Abstract

AbstractRice grain quality is a multifaceted quantitative trait that impacts crop value and is influenced by multiple genetic and environmental factors. Chemical, physical, and visual analyses are the standard methods for measuring grain quality. In this study, we evaluated high-throughput hyperspectral imaging for quantification of rice grain quality and classification of grain samples by genetic sub-population and production environment. Whole grain rice samples from the USDA mini-core collection grown in multiple locations were evaluated using hyperspectral imaging and compared with results from standard phenotyping. Loci associated with hyperspectral values were mapped in the mini-core with 3.2 million SNPs in a genome-wide association study (GWAS). Our results show that visible and near infra-red (Vis/NIR) spectroscopy can classify rice according to sub-population and production environment based on differences in physicochemical grain properties. The 702–900 nm range of the NIR spectrum was associated with the chalky grain trait. GWAS revealed that grain chalk and hyperspectral variation share genomic regions containing several plausible candidate genes for grain chalkiness. Hyperspectral quantification of grain chalk was validated using a segregating bi-parental mapping population. These results indicate that Vis/NIR can be used for non-destructive high throughput phenotyping of grain chalk and potentially other grain quality properties.