Visualization and prediction of TVB-N content in chilled pork by hyperspectral imaging

Abstract

TVB-N is one of the important indexes of the freshness of chilled pork. To present the freshness more accurately and completely, a visualization technology of chemical composition was applied and optimized for pork freshness detection. Two hyperspectral cameras covering visible and near-infrared wavebands were used to detect the TVB-N content. GA, SPA, iPLSR, and siPLSR were employed to select characteristic wavebands from the spectra data extracted from the hyperspectral images, and PLSR models were established on full wavebands and characteristic wavebands. According to the models established on the spectral data, TVB-N distribution maps were obtained, and URV and ICV were employed to evaluate the performance of distribution maps. The results showed that, both PLSR models established on the characteristic wavebands and full wavebands achieved good accuracy, and that the characteristic wavebands cluster was in the spectral range of 600-750 nm, 1020-1120 nm, and 1450-1570 nm. To optimize the performance of distribution maps, prediction models were reconstructed under the guidance of URV and ICV. This study provided a method of predicting TVB-N content of pork in both spectral and imaging aspect for online testing the freshness of chilled pork, which improves the efficiency and quality of testing.