Hyperspectral Imaging Detection of Total Viable Count from Vacuum Packing Cooling Mutton Based on GA and CARS Algorithms

In the process of spectral modeling, spectral extraction of characteristic bands with different variable screening algorithms is an important step for improving the model effects. Total viable count of cooling mutton under vacuum packing condition was chosen as the research index in this paper, while the influence of 2 variable screening algorithms on its hyperspectral PLS model effects was compared. Mutton muscle spectra of Regions of interest (ROIs) were extracted and preprocessed. Subsequently, Genetic Algorithm (GA) and Competitive Adaptive Reweighted Sampling (CARS) were applied to extract characteristic bands from preprocessed spectra at full band range of 473 similar to 1000 nm. Model effects of GA-PLS, CARS-PLS and W-PLS with corresponding bands selection were contrasted and analyzed. The results indicated that both model effects of GA-PLS, CARSPLS were better than that of W-PLS, and CARS-PLS model effect was optimal. As for the CARS-PLS model, the determination coefficient (RD and root mean square error (RMSEC) of calibration set was 0. 96 and 0. 29, and the determination coefficient (Rc(2)) and root mean square error (RMSECV) of leave-one-out cross validation was 0. 92 and 0. 46, respectively. Meanwhile, the determination coefficient (R-P(2)), root mean square error of prediction (RMSEP) and the ratio of standard deviation to standard error of prediction (RPD) of prediction set was 0. 92 and 0. 47 and 3. 58, respectively. Therefore, hyperspectral imaging (HSI) technology combined with CARS-PLS can achieve quick, non-destructive and accurate detection of mutton total viable count.