## 4 Discussion  
The study illustrates the effectiveness of rapid, nondestructive detection of sausage grades using the NIR bands at (874-1734 nm). Intact and sliced sausages were used to explore the feasibility of using hyperspectral imaging to grade Cantonese sausages in some studies [28; 29]. It was found that intact sausages had better classification effect than sliced sausages. In fact, there is a layer of intestinal skin outside intact sausages, which will interfere with the acquisition of spectrum, and sliced sausages can avoid this problem, so the result of sliced sausages should be better. However, the experimental results were different. The explanation is that the ROI of sliced sausages samples is small and variance of the proportion of lean and fat meat is large which have a great influence on the average spectrum. On the contrary, intact sausages are less affected, so the robustness of the model is better than that of sliced sausages. In this study, the k-means algorithm was used to segment lean meat and fat meat regions before modeling, which avoided the influence of the proportion of lean and fat meat and improved the robustness of the model. Preliminary separation of fat meat and lean meat was carried out by k-means, and then PLSR and SPA-MLR modeling of two separated sets and no-repartition sets were performed, respectively. Among them, the prediction of fat meat under SPA-MLR has the highest coefficient of decision (R\({}_{\text{p}}\)\({}^{2}\) = 0.9153). At the same time, all the calibration and prediction of the no-repartition set  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**ROI** & **R\({}_{\text{c}}\)\({}^{2}\)** & **RMSEC** & **R\({}_{\text{p}}\)\({}^{2}\)** & **RMSEP** & **D\({}_{\text{c}}\)** & **D\({}_{\text{P}}\)** \\ \hline All & 0.8656 & 0.1635 & 0.8387 & 0.2341 & 100\% & 96.15\% \\ Lean meat & 0.9371 & 0.1635 & 0.9153 & 0.2422 & 100\% & 100\% \\ Fat meat & 0.9320 & 0.1686 & 0.8955 & 0.2619 & 98.15\% & 96.15\% \\ \hline \end{tabular}
\end{table}
Table 2: Multiple linear regression (MLR) modeling result statistics for Cantonese sausage degree with NIR spectra using selected wavelengths extracted by successive projections algorithm (SPA).  
and fat meat set under PLRSR and the fat meat set under SPA-MLR reached 100%. From the results of the lean meat and fat meat sets modeled by PLRSR, it was found that the fat meat in sausage reflected the quality grade of the sausage better than that of lean meat. According to the effective wavelengths extracted by SPA and load weightings of PLRSR, we can see that the main bands of effective information are distributed around 950 nm, 1150 nm, 1300 nm, and that there are significant differences in the reflectance spectra of different grades of sausages. This study had proved that nondestructive detection using hyperspectral information can replace traditional chemical detection methods and achieve rapid classification of sausage grades. At the same time, the robustness of the model can be further improved by separating lean meat and fat.  
Conceptualization, Q.W. and Y.H.; methodology, Y.H.; software, Q.W.; validation, Q.W. and Y.H.; formal analysis, Y.H.; investigation, Q.W.; resources, Y.H.; data curation, Q.W.; writing--original draft preparation, Q.W.; writing--review and editing, Y.H.; visualization, Q.W.; supervision, Y.H.; project administration, Y.H.; funding acquisition, Y.H.  
This research was funded by National key R&D program of China 2018 YFD0101002.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SPA & successive projections algorithm \\ PLRSR & partial least squares regression \\ NIR & near infrared spectroscopy \\ MLR & multiple linear regression \\ ROI & region of interest \\ RMSE & root mean square error \\ STDEV & standard deviation \\ SPME & solid phase micro extraction process \\ GC-MS & gas chromatography-mass spectrometry \\ \end{tabular}