Identification and classification of buckwheat grain by microfocus radiography and hyperspectral imaging methods

Classification of buckwheat grains is important because the absence of defective grains is a guarantee of yield and quality. Buckwheat grains were randomly selected from a batch whose grains varied in quality. The identification and classification of buckwheat grains according to the degree of fulfillment was carried out by a combination of microfocus X-ray and hyperspectral image analysis and multivariate analysis techniques. Using microfocus radiography, buckwheat grains were categorized into groups according to the degree of fulfillment. Hyperspectral image of buckwheat grains in the range of 935-1720 nm was acquired using a Specim FX17 camera. Using the polygon selection function, the averaged spectra were obtained and a data matrix of grain samples was generated. The bands of the spectrum contributing most to the grading of the grain samples by degree of fulfillment were identified using the principal component analysis. The classification model of grading buckwheat grain into groups by degree of fulfillment was constructed by partial least squares discriminant analysis method. The results showed that hyperspectral image is a potential tool for rapid and accurate identification of buckwheat grains, which can be used in large-scale grain classification and grain quality determination.