Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process

This study aimed to develop simplified models for rapid and nondestructive monitoring TVB-N contents during drying of cured meats based on a hyperspectral imaging system (HSI) in the spectral range of 400-1000 nm. Multivariate calibration models were first developed using partial least-squares regression (PLSR) and least-squares support vector machines (LS-SVM) in the full spectral range. In order to simplify the calibration model, a set of 9 feature wavelengths was then selected using the regression coefficient and two simplified models using PLSR and multiple linear regression (MLR) were subsequently established. The best simplified model obtained was based on MLR, with an R2p of 0.861 and RMSEP of 4.73. Mapping of TVB-N contents was realized by transferring the quantitative model to each pixel in the image to display protein degradation in cured pork slices at different drying periods. The results provided a possibility of realizing a multispectral imaging technique for on-line monitoring the TVB-N contents of cured meats during drying processes.