## 3 Results and Discussion
### Prediction Results of the Model  
As is mentioned above, in order to obtain an accurate and efficient model for the detection of TVC in pork meat using hyperspectral image technology, two approaches including SVR and PLSR were studied systematically and comparatively. At the same time, some typical spectral preprocessing methods, such as standard normalize transformation (SNV), multiple scatter correction (MSC), first derivative (1D), second derivative (2D), haar transformation (HT), and centering transformation (CT) were all applied to improve the accuracy of the prediction model. Meanwhile, only one pre-treatment was used at one time here to reduce the calculation; the hyperspectral data volume is very large especially when acquisition is continuous. The statistical results are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Pre-treatment**} & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} \\  & & \(\mathbf{R_{C}}\) & **SEC** & \(\mathbf{R_{P}}\) & **SEP** \\ \hline \multirow{8}{*}{PLSR} & Raw spectra & 0.95 & 0.4628 & 0.92 & 0.9520 \\  & SNV & 0.96 & 0.4221 & 0.90 & 0.9807 \\ \cline{1-1}  & MSC & 0.96 & 0.4234 & 0.90 & 0.9803 \\ \cline{1-1}  & 1D & 0.95 & 0.4876 & 0.94 & 0.8187 \\ \cline{1-1}  & 2D & 0.96 & 0.4198 & 0.94 & 0.7374 \\ \cline{1-1}  & HT & 0.95 & 0.4628 & 0.92 & 0.9520 \\ \cline{1-1}  & CT & 0.97 & 0.4055 & 0.91 & 0.8709 \\ \hline \multirow{8}{*}{SVR} & Raw spectra & 0.87 & 0.7741 & 0.72 & 0.9419 \\ \cline{1-1}  & SNV & 0.98 & 0.2929 & 0.94 & 0.6297 \\ \cline{1-1}  & MSC & 0.99 & 0.2667 & 0.94 & 0.5903 \\ \cline{1-1} \cline{2-6}  & 1D & 0.99 & 0.0881 & 0.95 & 0.5264 \\ \cline{1-1}  & 2D & 0.99 & 0.0004 & 0.94 & 0.4570 \\ \cline{1-1}  & HT & 0.84 & 0.8355 & 0.63 & 1.0397 \\ \cline{1-1}  & CT & 0.87 & 0.7741 & 0.72 & 0.9419 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of prediction models (partial least squares regression (PLSR) model and support vector machine regression (SVR) model) with different pre-treatments. SNV: standard normalize transformation; MSC: multiple scatter correction; 1D: first derivative; 2D: second derivative; HT: haar transformation; CT: centering transformation; SEC: standard error of calibration set; SEP: standard error of prediction set. Figure 3: Raw spectra of all the pork meat samples from the first day to the fifteenth day; the spectra are the averaged value of the ROI. As is shown in Table 2, it was demonstrated that the PLSR model with 2D had better performance than those of the other pretreatments, with the higher correlation coefficients of 0.99 and 0.94 as well as the lower SE (standard error) of 0.4198 and 0.7374 log CFU/g for calibration set and prediction set, respectively. As is shown in Figure 4, peaks in the VIP (Variable importance projection) score plot of the optimized PLSR model provided the information about the chemical components responsible for the reproduction of bacteria. For instance, the peak near 715 nm was the fourth overtone of Aromatic Hydrocarbon C-H stretching. The wavelength near 730 nm was assigned to moisture absorption, corresponding to the O-H stretching third overtone., and the band located at approximately 760 nm was related to the fourth overtone of C-H stretching. Figure 5a is a scatter plot that shows a correlation between reference measurement and the best PLSR model predicted in the calibration and prediction sets. The best SVR model with 2D achieved the highest correlation coefficients of 0.99 and 0.94 as well as the lowest SE of 0.0004 and 0.4570 for calibration set and prediction set, respectively. Figure 5b is a scatter plot that shows a correlation between reference measurement and best SVR model predicted in the calibration and prediction sets. According to the comparison of the PLSR model and the SVR model, the SVR model with 2D pretreatment was selected as the final prediction model of TVC in pork meat with hyperspectral image technology.