Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging

Total viable count (TVC) of bacteria is one of the most important indexes in evaluation of quality and safety of meat. In this work, the TVC in pork meat was detected by hyperspectral imaging technology. First, the spectra were extracted from 3-D datacube of hyperspectral image and 100 characteristic variables were selected by synergy interval PLS (SI-PLS) algorithm. Meanwhile, principal component analysis (PCA) was implemented on the 3-D datacube to determine 3 characteristic pictures. And, 5 characteristic variables were extracted using texture analysis from each characteristic picture. PCA was implemented on 111 spectra variables, 15 image variables and data fusion (126 variables), and the top principal components (PCs) were extracted for developing the TVC prediction model, respectively. Experimental results show that the model based on data fusion is superior to others, which was achieved with RMSEP=0.243lgCFU/g and Rp2=0.8308 in the prediction set. This work demonstrates that HSI technique, as a nondestructive analytical tool, has the potential in nondestructive detection of TVC in pork meat. [Display omitted] •Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging•Data fusion based on spectral and image information from hyperspectral data•Construction of nonlinear regression model based on data fusion