Abstract: In order to quickly and non-destructively identify the freshness of imported chilled beef, we applied hyperspectral imaging technology combined with chemometrics to detect the characteristic freshness indexes. Firstly, chilled beef with different storage times were evaluated for their total volatile basic nitrogen (TVN-N) content, color parameters (L*, a* and b*), and deoxymyoglobin, oxymyoglobin and metmyoglobin contents. The characteristic freshness indexes were selected from them by significance and correlation analysis. Then, hyperspectral imaging technology was used to collect spectral and image information of samples. Based on the spectral data, partial least squares, interval partial least squares, competitive adaptive reweighted sampling-partial least squares (CARS-PLS) models were developed to predict the characteristic indicators, and the prediction results from these models were compared. The results showed that TVB-N content, a* and b* were selected as the characteristic freshness indexes of chilled beef with different storage times. The best prediction model for TVB-N content, a* and b* was CARS-PLS, with correlation coefficient of calibration (rc) values of 0.965 8, 0.949 5 and 0.964 2; root mean square error of cross-validation (RMSECV) values of 1.06 mg/100 g, 0.7 and 0.76; correlation coefficient of prediction (rp) values of 0.963 7, 0.949 4 and 0.942 3; and root mean square error of prediction (RMSEP) values of 1.12 mg/100 g, 0.72 and 0.77 respectively. The CARS-PLS model combined with image processing was used to draw a visual distribution map for these characteristic indicators, which visually represented the trend of freshness of chilled beef during storage. This study proves that hyperspectral imaging technology allows the rapid detection and distribution visualization of chilled beef freshness indexes.

Key words: imported chilled beef; characteristic freshness indexes; hyperspectral imaging; rapid detection; visualization