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Development of short-wavelength near-infrared spectral imaging for grain color classification

Color class of wheat is an important attribute for the identification of cultivars and the marketing of wheat, but is not always easy to measure in the visible spectral range because of variation in vitreosity and surface structure of the kernels. This work examines whether short-wavelength near IR imaging in the range 632-1098 nm can be used to distinguish different cultivars. The spectral characteristics of six hard white winter and hard red spring wheats were first studied by bulk-sample SW-NIR reflectance spectroscopy using regression analysis to select appropriate wavelengths and sets of wavelengths. Prediction of percent red wheat was better if C-H or O-H vibrational overtones were included in the models in addition to the tail from the visible chromophore absorbance, apparently because the vibrational bands make it possible to normalize the color measurement to the dry matter content of the samples. Next, a reflectance spectral image of 640 X 480 spatial pixels and 11 wavelengths was acquired for a mixture of the two contrasting wheat samples using a CCD camera and a liquid crystal tunable filter. The cultivars were distinguished in the image of principal component (PC) score number two that was calculated from the spectral image. The discrimination is due to the tail from the absorbance band that peaks in the visible. PC images 3 and 6 seem to arise mainly from O-H and C-H bands, respectively, and it is speculated that these spectral features will be important for generating multivariate models to predict the color class of grain. It is shown that the contrast between the red-wheat, white- wheat and background can be increased by applying histogram equalization and segmentation of the kernels in the images.