The effect of temperature on the identification of NIR animal fats and oils species and its mechanism

Abstract

This study aimed to investigate the effect of temperature on the near-infrared spectroscopy (NIRS) discriminant of animal fats and oils species and its mechanism. Based on samples of lard oil, chicken oil, beef tallow, and mutton tallow, the classification errors of two-class (non-ruminant, ruminant) and three-class (lard oil, beef tallow, mutton tallow) at different temperatures were compared by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The results showed that ruminant and non-ruminant animal fats were completely distinguished at 55 °C with a 0.000 classification error. Good classification results were achieved at 80 °C for lard oil, beef tallow, and mutton tallow, with a reduction of 0.142, 0.181, and 0.121 classification errors, respectively, compared to 30 °C. The coupling chemometrics method of near-infrared spectroscopy effectively explores the wavelength points and bands that contribute to identifying species at different temperatures and the corresponding structural changes. The temperature mechanism is analyzed to provide an efficient and accurate analysis method and theoretical support for the quality control of animal oils and fats.