Black pepper is the most important spice and used as medicinal plant. Hyperspectral imaging is a fast, accurate, and cheap method for the assessment of food material. The present research offers the possibility of using hyperspectral imaging for the detection of chickpea flour, wheat flour, and sea foam adulterants in black pepper powder. The adulterated samples were prepared with 0–50 % levels. The principal component analysis method was applied to select the efficient wavelengths of the acquired hypercubes of the samples. After feature extraction, sequential feature selection method was used to find efficient features and then artificial neural network was used for classification and prediction. The classification and prediction accuracy of the best model was 100 %. The obtained results in the present research demonstrated the high capability of the developed system for detecting adulterants in black pepper powder.
