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 473～1 000 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, CARS-PLS were better than that of W-PLS, and CARS-PLS model effect was optimal. As for the CARS-PLS model, the determination coefficient (R2c) and root mean square error (RMSEC) of calibration set was 0.96 and 0.29, and the determination coefficient (R2cv) and root mean square error (RMSECV) of leave-one-out cross validation was 0.92 and 0.46, respectively. Meanwhile, the determination coefficient (R2p), 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.

[Study on the Rapid Evaluation of Total Volatile Basic Nitrogen (TVB-N) of Mutton by Hyperspectral Imaging Technique].

Total Volatile Basic Nitrogen (TVB-N) was usually taken as the physicochemical reference value to evaluate the mutton freshness. In order to explore the feasibility of hyperspectral (HSI) imaging technique to detect mutton freshness, 71 representative mutton samples were collected and scanned using a diffuse reflectance hyperspectral imaging (HSI) system in the Visible-Near infrared (NIR) spectral region (400-1 000 nm), and the chemical values of TVB-N content were determined using the semimicro Kjeldahl method according to the modified Chinese national standard. The representative spectra of mutton samples were extracted and obtained after selection of the region of interests (ROIs). The samples of calibration set and prediction set were divided at the ratio of 3 : 1 according to the content gradient method. Optimum HSI calibration models of the mutton (TVB-N) were established and evaluated by comparing different spectral preprocessing methods and modeling methods, which included Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) methods. The results are that through the utilization of Multiplicative Scatter Correction (MSC), first derivative, Savitzky-Golay (S-G) smoothing and mean-centering together, both PLSR and PCR were able to achieve quantitative detection of mutton TVB-N. As for the PLSR model of mutton TVB-N established, the spectral pretreatment methods chosen included MSC, first derivative, S-G (15,2) smoothing and mean-centering, and the latent variables (LVs) number used was 11. As for the calibration set of PLSR model of mutton TVB-N, the correlation coefficient (r) and root mean square error of calibration (RMSEC) were 0.92 and 3.00 mg x (100 g)(-1), respectively. As for the prediction set of PLSR model of mutton TVB-N, the correlation coefficient (r), Root Mean Square Error of Prediction (RMSEP), and ratio of standard deviation to standard error of prediction (RPD) were 0.92, 3.46 mg x (100 g)(-1) and 2.35, respectively. The study demonstrated that the rapid and accurate analysis of TVB-N, the key freshness attribute, could be implemented by using the hyperspectral imaging (HSI) technique. The study provides the basis for further rapid and non-destructive detection of other mutton freshness attributes by using the hyperspectral imaging (HSI) technique, the improvement of current modeling effect of TVB-N content and the application involved of the technique in the practical production.

[Effects of Regions of Interest (ROIs) on Detection Models of Mutton pH Based on Hyperspectral Imaging].

Selection of Regions of interest (ROIs) and subsequent spectral extraction was a key step of non-destructive detection and analysis based on hyperspectral imaging (HSI). For the rapid and accurate detection of mutton pH, the study on the effects of 2 different ROIs on mutton pH models was carried out in the visible-near infrared region of 473～1 000 nm. 2 ROIs methods of Rectangle Regions (RR) and Image Segmentation (IS) were adopted to extract 122 corresponding representative spectra respectively. The influence of different preprocessing methods and ROIs methods on 3 pH models, including stepwise multiple linear regression (SMLR), principal component regression (PCR) and partial least squares regression (PLSR), was compared and analyzed. The results indicated that SMLR and PLSR model performance was optimal in 3 models established with spectral data extracted from Rectangle Regions (RR) and Image Segmentation (IS) respectively. As for the SMLR model, corresponding to the RR ROIs method, the correlation coefficient (Rcal) and root mean square error (RMSEC) of calibration set was 0.85 and 0.085 respectively, and the correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set was 0.82 and 0.097 respectively. As for the PLSR model, corresponding to the IS ROIs method, the correlation coefficient(Rcal) and root mean square error (RMSEC) of calibration set was 0.95 and 0.050 respectively, and the correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set was 0.91 and 0.071 respectively. By comparing the modeling results of spectral data extracted from 2 ROIs methods, the modeling performances of Image Segmentation (IS) were always better than Rectangle Regions (RR) in all the 3 modeling methods. The study shows that it is feasible to apply hyperspectral imaging technology combined with the ROIs method of Image Segmentation (IS) to accurate, fast and non-destructive detection of mutton pH.

[Study on Characteristic Bands Selection of Lamb pH Value Based on Hyperspectral Imaging and Partial Least Squares(PLS)].

Characteristic bands method selection and subsequent spectral extraction has a great influence on the hyperspectral model performance. For rapid and accurate detection of mutton pH value, the effects of 2 band-selection methods on PLS models of mutton pH based on HSI technique were carried out and discussed. Initially, the preprocessing method of second derivative (2D), multiplicative scatter correction (MSC) and mean-centering together was implemented on the representative spectra of mutton muscle portion. Then, 2 methods of synergy interval partial least square (siPLS) and the combination of synergy interval partial least squares with genetic algorithm (siPLS-GA) were used to extract the characteristic bands in the spectral range of 473～1 000 nm. Finally, 2 PLS models of lamb pH value were established with the corresponding characteristic bands, and were also compared with the effect of full-band PLS model. The results indicated that the effect of siPLS-GA-PLS model was the best. As for the siPLS-GA-PLS model, 56 characteristic wavelength points were chosen, the correlation coefficient(Rcal) and root mean square error(RMSEC) of calibration set was 0.96 and 0.043 respectively, and the correlation coefficient(Rp) and root mean square error(RMSEP) of prediction set was 0.96 and 0.048 respectively. Spectral variables were reduced and model accuracy was improved. It can be concluded that characteristic bands selection and rapid and accurate detection of lamb pH can be achieved using hyperspectral imaging technique combined with siPLS-GA method.