Feasibility of Authenticating Mutton Geographical Origin and Breed Via Hyperspectral Imaging with Effective Variables of Multiple Features

Muttons of specific geographical origin and breed are nutritious and flavorful and they have high economic value. However, substitution and adulteration are pervasive in the market. In this study, we explore hyperspectral imaging for authenticating mutton geographical origin and breed. We extracted the reflectance spectra of 400–1000 nm from the region of interest (ROI) of hyperspectral images, and we selected effective wavelengths through principal component analysis. Then, we acquired textural features from grayscale ROI images using a gray-level gradient co-occurrence matrix and screened effective variables through correlation analysis. With various features, we used support vector machines, random forest (RF), K-nearest neighbor, and convolutional neural network to develop models to identify mutton geographical origin and breed. Optimal identification was achieved using RF with the spectra of effective wavelengths and effective variables of textural features. Classification accuracies of 99.54% and 95.67% for calibration and prediction sets, respectively, were obtained. From the confusion matrix, we authenticated each mutton type with high accuracy. Hence, HSI with effective variables of multiple features coupled with RF can identify mutton geographical origin and breed accurately and provide a potential reference for a simple system that integrates multi-wavelength spectroscopy and digital imaging.