Classification of contaminants from wheat using near-infrared hyperspectral imaging

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Near-Infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains (2018, background): “An example of this approach was reported for the detection of impurities in wheat by HSI in the NIR range 1000–1600 nm (33).”
Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics (2019, methodology): “There reportedly exist certain redundant characteristic variables among hundreds of spectral wavebands, which might slow down the modeling speed and influence the model’ performance [17,21].”
Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, methodology): “Ravikanth et al. (2015) used a similar approach for extracting the mean spectral reflectance to differentiate the contaminants in wheat grains using near-infrared (NIR) imaging (1000–1600 nm).”