Abstract
A plant breeding program needs to evaluate a large number of materials for different traits within a limited time. Near-infrared (NIR) spectroscopy has been used to quickly determine seed composition in various crop species. In this study, we compared whole-seed evaluations of protein and oil content by NIR methods in soybean [Glycine max (L.) Merr.], and then discussed the application to plant breeding. The differences among the entries tested were highly significant in both traits for each method used. No significant difference but high correlation and consistency existed between DA 7250 and wet-chemistry methods. Compared with DA 7250, ZX-50 exhibited, to some extent, differences or errors. The differences of ZX-50 methods were found to be correlated with seed sizes and could be corrected using regression equations formulated for bias calculation. After correction, the differences in the predictions between DA 7250 and ZX-50 methods were insignificant. Similar to DA 7250, ZX-50 methods exhibited a high repeatability (> 98%) of the predictions. By validation with 760 bulk samples of different seed types and 240 single-plant samples, it further demonstrated that as a non-destructive, fast and cost-efficient method, ZX-50 NIR analysis with an appropriate bias correction could be used in soybean breeding, specifically suitable for single plant selection based on whole seeds.
Highlights
IntroductionA breeder needs to assess large numbers (thousands and/or tens of thousands) of breeding materials for multiple traits within a limited period of time
In modern plant breeding, a breeder needs to assess large numbers of breeding materials for multiple traits within a limited period of time
Analysis of variance (ANOVA) with the original predictions indicated that the differences among methods were highly significant (p < 0.01) for both protein and oil content (Table 1), but no significant difference existed between DA 7250 and wet-chemistry
Summary
A breeder needs to assess large numbers (thousands and/or tens of thousands) of breeding materials for multiple traits within a limited period of time. Rapid and robust phenotypic evaluation is still a great challenge and a realistic demand in practical breeding for many quantitative traits of importance including nutrient components of crops [1]. High throughput phenotyping helps breeders to efficiently perform evaluations and timely select the desired genotypes in the breeding populations with complicated variations. Soybean [Glycine max (L.) Merr.] is a major crop grown worldwide and plays an important part in the agricultural production, human food security and international trade. Soybean seed consists mainly of protein, oil, carbohydrates, minerals and water. Approximately 40% and 20% of dry seed weight in soybean are protein and oil, respectively. High-oil cultivars are expected for vegetable-oil processing and/or industrial uses such as biodiesel, while high protein content is usually preferred in human diet and soy-based food industries. The physiochemical characteristics of seed may affect soybean price [3]
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