Dataset Information

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

ABSTRACT: Rice 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.

SUBMITTER: Barnaby JY

PROVIDER: S-EPMC7283329 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Barnaby Jinyoung Y JY Huggins Trevis D TD Lee Hoonsoo H McClung Anna M AM Pinson Shannon R M SRM Oh Mirae M Bauchan Gary R GR Tarpley Lee L Lee Kangjin K Kim Moon S MS Edwards Jeremy D JD

Scientific reports 20200609 1

Rice 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 grow ...[more]

PMID: 32518379

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Project description:Although great progress has been made in the development of hybrid rice with increased yield, challenges for the improvement of grain quality still remain. In this study, the textural properties of cooked rice and physicochemical characteristics of starch were investigated for 29 new hybrid rice derived from 5 sterile and 11 restorer rice lines. Except for one sterile line Te A (P1) with high apparent amylose content (AAC) (26.9%), all other parents exhibited a low AAC. Gui 263 demonstrated the highest AAC (20.6%) among the restorer lines, so the Te A/Gui 263 hybrid displayed the highest AAC (23.1%) among all the hybrid rice. The mean AAC was similar between sterile, restorer lines and hybrid rice. However, the mean hardness of cooked rice and gels of sterile lines were significantly higher than that of restorer lines and hybrid rice (p < 0.05). Pasting temperature and gelatinization temperatures were significantly higher in the hybrids than in the restorer lines (p < 0.05). Cluster analysis based on the physicochemical properties divided the parents and hybrid rice into two major groups. One group included P1 (Te A), P12 and P14 and three hybrid rice derived from P1, while the other group, including 39 rice varieties, could be further divided into three subgroups. AAC showed significant correlation with many parameters, including peak viscosity, hot peak viscosity, cold peak viscosity, breakdown, setback, onset temperature, peak temperature, conclusion temperature, enthalpy of gelatinization, gel hardness and cooked rice hardness (p < 0.05). Principal component analysis revealed that the first component, comprised of the AAC, peak viscosity, breakdown, setback, onset temperature, peak temperature, conclusion temperature and gel hardness, explained 44.1% of variance, suggesting AAC is the most important factor affecting the grain quality of hybrid rice. Overall, this study enables targeted improvements to key rice grain quality attributes, particularly AAC and textural properties, that will help to develop superior rice varieties.

Dataset Information

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

Publications

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

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