Avena sativa L. is valuable vital cereal and sustainable protein source. The impurity Avena fatua L. makes a great contribution to the decrease in the quality of Avena sativa L. grain. Traditional methods for detecting and quantifying impurities are laborious and do not provide sufficient speed and accuracy. In this regard, imaging spectroscopy is becoming increasingly popular, including because of the ability to simultaneously analyse the morphological and spectral features of a mixture of grains. In this paper, we investigate the feasibility of an imaging spectroscopy method for the analysis of grains impurity Avena fatus L. in Avena sativa L. We have presented an approach based on a spectral image acquisition and digital processing for the detection and quantification of grains impurity. Proposed technique may complement conventional grain assessment and sorting methods and become especially effective for a commercial grain batches. Hyperspectral, multispectral and RGB datasets were compared in terms of suitable number of spectral channels to quantify grains impurity, and it was emphasized that eight spectral bands compared to a hundred do not show a significant effectiveness reduction of making correct decisions. We believe that this will facilitate the development of technologically efficient devices for such a task.