Quantitative detection of fox meat adulteration in mutton by hyper spectral imaging combined with characteristic variables screening

This study aims to explore the rapid nondestructive detection of fox meat adulteration in minced mutton using hyperspectral imaging technology combined with characteristic variables screening. A quantitative detection model was also established. A total of 120 adulterated mutton samples were first prepared by adding fox meat into minced mutton at different levels, including 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%. Spectral information of samples with different adulterated contents was then obtained using visible near-infrared hyperspectral imaging. Some approaches were selected for spectral pre-processing, including First Derivative (1D), the Mean Center (MC), Multiplicative Scattering Correction (MSC), and Standard Normalized Variate (SNV). A Partial Least Squares Regression (PLSR) model was established to determine the 1D optimal pre-processing using the original and the pre-processed spectra. The prediction performance of model was significantly improved, where the determination coefficient (R2) value of calibration set increased from 0.925 to 0.940, the R2 value of cross-validation set increased from 0.894 to 0.911, the R2 value of validation set increased from 0.896 to 0.912, and the relative error increased from 2.37 to 2.73, indicating better prediction ability of model, compared with no pre-processed spectra. The pre-processed spectra effectively enhanced the difference of spectral data. There were also obvious absorption and reflection bands at specific wavelengths. Furthermore, Genetic Algorithm (GA), Competitive Adaptive Reweighted Sampling (CARS), and Two-Dimensional Correlation (2D-COS) analysis were used to screen the characteristic wavelengths after 1D pre-processing. The PLSR and Support Vector Regression (SVR) models were then established to compare with the total 846 wavelengths and characteristic ones. It was found that 207, 34, and 14 characteristic wavelengths were obtained by GA, CARS, and 2D-COS. More importantly, the performances of all SVR models using the whole wavelengths and characteristic wavelengths were better than that of PLSR model. Among them, the best performance was achieved in the SVR model with 14 characteristic wavelengths from 2D-COS, where the R2 value and root mean square error of cross-validation set were 0.928 and 3.00%, respectively, while the relative error of validation set was 4.85. Consequently, the hyperspectral imaging combined with 2D-COS-SVR model can effectively realize the quantitative detection of the fox meat adulterated in the minced mutton. The findings can also provide a strong technical support for the development of a low-cost meat adulteration detection system.