Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning

Commercial jerky counterfeiting is widespread in the market. This study combined visible-near-infrared and short-wave-near-infrared hyperspectral imaging along with multiple machine learning algorithms for non-destructive identification of five types of commercial jerky products, and explored the impact of different spectral bands, algorithm selection, and optimization methods on identification performance. After data preprocessing, all models' accuracies and stability improved. Specifically, the logistic regression model was best for jerky identification, with 85.78 %-100.00 % accuracy. With hyperparameter optimization, Support Vector Machine with linear kernel had highest accuracy (89.29 % and 95.29 % in two bands). Additionally, the artificial neural network with the hyperbolic tangent activation function had optimal training performance, exceeding 90.00 % accuracy. The findings demonstrate short-wave-near-infrared hyperspectral imaging combined with linear models (logistic regression and Support Vector Machine with linear kernel parameter settings) is better for identifying the types of jerky. This study provides reference for the band, model selection, and optimization of jerky type identification.