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Preserving red-chili quality is of utmost importance in which the authoritiesdemand the quality techniques to detect, classify and prevent it from theimpurities. For example, salt, wheat flour, wheat bran, and rice brancontamination in grounded red chili, which typically a food, are a seriousthreat to people who are allergic to such items. This work presents thefeasibility of utilizing visible and near-infrared (VNIR) hyperspectral imaging(HSI) to detect and classify the aforementioned adulterants in red chili.However, adulterated red chili data annotation is a big challenge forclassification because the acquisition of labeled data for real-time supervisedlearning is expensive in terms of cost and time. Therefore, this study, for thevery first time proposes a novel approach to annotate the red chili samplesusing a clustering mechanism at 500~nm wavelength spectral response due to itsdark appearance at a specified wavelength. Later the spectral samples areclassified into pure or adulterated using one-class SVM. The classificationperformance achieves 99% in case of pure adulterants or red chili whereas 85%for adulterated samples. We further investigate that the single classificationmodel is enough to detect any foreign substance in red chili pepper rather thancascading multiple PLS regression models.

Unsupervised adulterated red-chili pepper content transformation for hyperspectral classification