Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Abstract

Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For example, salt, wheat flour, wheat bran, and rice bran contamination in grounded red chili, which though are food items, are a serious threat to the people who are allergic to such items. Therefore, this work presents the feasibility of utilizing Visible and Near Infrared (VNIR) Hyperspectral Imaging (HSI) to detect and classify such adulterants in grounded red chili. This study, for the very first time, proposes a novel approach to annotate the grounded red chili samples using a clustering mechanism at a 550 nm wavelength spectral response due to its dark appearance at a specified wavelength. Later the spectral samples are classified into pure or adulterated using one-class SVM. The classification performance achieves 99% in the case of pure adulterants and/or red chili whereas 85% for adulterated samples. We further investigate that the single classification model is enough to detect adulterants in red chili powder compared to cascading multiple PLS regression models.