Rice is a major world food staple and there is increasing interest in producing rice grains with pigmented bran colors, black/purple or red, which are rich in antioxidants, providing human health benefits. Identification of bran color to perform breeding selections requires the removal of the outer hull, which is a destructive process. Being able to detect bran color without dehulling would have advantages in breeding as well as in quality control of seed rice production for both brown and colored bran varieties. In this study, we explored single-kernel NIR spectroscopy and NIR hyperspectral imaging for rapid and non-destructive prediction of rice bran color. Color (L*, a*, and b*) values of dehulled rice samples were measured and rough rice samples were scanned using SKNIR and NIR hyperspectral imaging. The prediction results showed that SKNIR and hyperspectral imaging can be potentially used for efficient sorting of rough rice according to bran color.
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