## 3 Results
### Spa-Mlr  
#### 3.3.1 Effective Wavelengths Selection  
Due to the high correlation between adjacent bands of hyperspectral data, there is siginficant information redundancy in full-band modeling. Therefore, modeling using effective wavelengths is more effective than full band. In this study, SPA is used to extract the characteristic band, and then the characteristic band is modeled by MLR. The Savitzky-Golay (S-G) filter is a commonly used low-pass signal within the time domain filter. It is suitable for smoothing continuous signals and has the characteristics of fast calculation and strong operability [27]. Thus, the hyperspectral data preprocessed by S-G filter can effectively utilize the information in adjacent spectra with less noise. The parameter polynomial order of the S-G filter is two, the frame length is 15, and the preprocessed characteristic spectrum of 242-dimensional is obtained. The head and tail part of the spectrum is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ROI** & \(\text{R}_{\text{c}}{}^{2}\) & **RMSEC** & \(\text{R}_{\text{P}}{}^{2}\) & **RMSEP** & \(\text{D}_{\text{c}}\) & \(\text{D}_{\text{P}}\) \\ \hline All & 0.9890 & 0.0831 & 0.7775 & 0.1828 & 100\% & 100\% \\ Lean meat & 0.9881 & 0.0865 & 0.6827 & 0.2209 & 100\% & 92.31\% \\ Fat meat & 0.9875 & 0.0885 & 0.8991 & 0.1996 & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Result statistics for Cantonese sausage degree with near infrared spectroscopy (NIR) spectra extracted from samples using the (928–1686 nm) bands from the PLSR models, which include coefficient of determination of the training set (\(\text{R}_{\text{c}2}\)), root mean square error of the training set (RMSEC), coefficient of determination of the prediction set (\(\text{R}_{\text{P}2}\)), root mean square error of the prediction set (RMSEP), discrimination rate of the training set (\(\text{D}_{\text{c}}\)), discrimination rate of the prediction set (\(\text{D}_{\text{P}}\)).  
Figure 4: First four loading weights of the best fit partial least squares regression (PLSR) model of no-repartition. The variable X denotes the near-infrared reflectance spectrum; the variable Y denotes the sausage grade.  
truncated in preprocessing, but considering that the head and tail contain significant noise, truncation of these two parts does not have much effect on the modeling accuracy.  
Ten effective wavelengths were extracted from the hyperspectral data of 80 samples without repartition by SPA, which contained (897.87, 907.93, 921.34, 1008.58, 1095.92, 1213.65, 1341.69, 1598.43, 1683.07, 1710.18 nm). After the separation of lean meat and fat meat by k-means, the effective wavelengths were extracted by SPA respectively. The effective wavelengths of lean meat contained (907.93, 911.28, 921.34, 934.76, 968.30, 1022.01, 1116.09, 1156.44, 1338.32, 1422.67, 1588.28, 1710.18 nm), and the other contained (897.87, 907.93, 917.99, 951.53, 1082.47, 1163.9, 1304.6, 1453.06, 1588.28 nm). It was found that there were common selected bands in two groups, such as 907.93 nm, 1588.28 nm. The reason is that lean meat and fat meat are mainly composed of C, H, O elements, and the C=O and C-H formed by them are abundant in protein and fat. However, all the selected wavelengths of the two parts had a large difference which reflected the difference between the lean meat and fat meat.