NIR spectroscopy has been successfully used to determine moisture and protein contents in grains while imaging has been successful in determining spatial or morphological characteristics of grains such as size and shape of kernels, and speckiness in noodles. Grading factors, such as kernel vitreousness, that determine value of the grain are seldom uniformly distributed within a sample making spatial information critical in sample analysis. Conventional imaging is not sensitive or powerful enough to detect some of the grading factors. To deal with this issue, GRL has recently started investigating the use of hyperspectral imaging (HSI) for quantification of grain damage due to difficult-to-detect commonly encountered grading factors.

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