Brand discrimination of pet food via near-infrared spectroscopy combined with modified SPA-SI information enhancement

Abstract

For pet food consumers, it is essential to identify authentic brands quickly, simply, and cost-effectively. In this study, 353 pet food samples from four brands were selected, and high-precision brand discrimination was achieved using near-infrared (NIR) spectroscopy. At the same time, the method of information enhancement (IE) is proposed to enhance the information of 9 characteristic wavelengths selected by the successive projections algorithm (SPA), and the random forest (RF) discrimination model is established. The results show that the m-SPA-SI3-RF strategy proposed by us has the best model performance, with a total accuracy of 94.32% and a single accuracy of 88~100%, which is far better than the discrimination performance of full-RF (Total: 92.05%) and SPA-RF (Total: 89.77%). The number of wavelengths was reduced from 183 to 9, a 95.08% decrease, providing a basis for developing spectral equipment for discrimination between low-cost pet food brands. This study not only achieved spectral discrimination of pet food brands for the first time but also provided a new method for pet food brand discrimination, expanded the application potential of spectral analysis technology in pet food detection, and laid the foundation for the development of low-cost, specialized spectral equipment.