Abstract:To quickly and accurately determine the freshness of cooked beef during cold storage, the hyperspectral imaging (HSI) technique was used here to quantify and visualize total volatile basic nitrogen (TVB-N) content of cooked beef. Hyperspectral images of beef samples were captured in the range of 400–1000 nm, and data at six wavelengths (variables) with spectral characteristics were extracted by variable combination population analysis (VCPA). According to feature band images, 18 texture parameters were extracted using the Tamura algorithm, and nine color characteristics in the red (R), green (G), and blue (B) component images were calculated based on the RGB model. Particle swarm optimization and the least squares support vector machine (PSO-LS-SVM) algorithm were used to construct a TVB-N content prediction model from different combinations of variables. After analysis and comparison, the PSO-LS-SVM model based on a combination of spectral and color features showed the best predictive ability, and the determination coefficient (R2p) and root mean square error of prediction (RMSEP) were 0.955 and 1.093, respectively. Finally, the optimal model was applied to visualize TVB-N content. The study revealed that it is feasible to accurately predict and visualize TVB-N content of cooked beef by combining the spectral and color features of HSI and the PSO-LS-SVM algorithm, and this study can serve as a theoretical reference for analysis of freshness of other meats and meat products.
