## 3 Results and Discussion
### Performance of Regression Models with Different Pretreatment Methods  
This study compares 240 regression models with eight regression algorithms and 29 pretreatment transformations for the VIS-NIR spectrum. Figure 4 exhibits the \(R^{2}\) values for the training and testing sets of the regression models, and Figure 4a,b represent the prediction \(R^{2}\) values for the training and testing datasets, respectively. With different pretreatment methods, Table 4 and Figure 5 exhibit the RPD levels and RPIQ of the regression models. Figure 3: Average spectral contrast map of different pretreatment transformations. (**a**) Average spectrum without the Savitzky–Golay(SG) method; (**b**) average spectrum with the SG method. Figure 4: The accuracy of regression models with training (**a**) and testing (**b**) datasets by all pretreatment methods. Figure 5: The RPD level of regression models with different pretreatment methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Pretreatment Methods** & **PLS\_Linear** & **PLS\_Sigmoid** & **PLS\_RBF** & **SVR\_Linear** & **SVR\_Sigmoid** & **SVR\_RBF** & **GMRT** & **AdaBoost** \\ \hline RS & B & B & B & C & D & B & D & D \\ FD & C & D & D & D & D & D & A & A \\ SD & C & D & D & D & D & D & B & B \\ SNV & B & B & A & D & C & B & A & B \\ MSC & C & B & A & C & C & B & A & B \\ LG & B & B & A & B & C & B & D & D \\ MC & B & B & A & C & C & B & D & D \\ DT & B & B & A & C & C & A & B & B \\ SNV + DT & B & B & A & D & C & A & B & B \\ SNV + FD & C & C & D & D & D & D & A & A \\ SNV + SD & C & D & D & D & D & D & B & A \\ MSC + FD & C & D & D & D & D & D & B & A \\ MSC + SD & D & D & D & D & D & D & B & B \\ LG + FD & C & D & D & D & D & D & A & A \\ LG + SD & B & D & D & D & D & D & B & B \\ SG & B & B & B & C & D & B & D & D \\ SG + FD & C & B & B & D & C & D & A & A \\ SG + SD & C & D & D & D & D & B & B \\ SG + SNV & B & B & A & C & B & B & A & B \\ SG + MSC & D & B & A & B & D & B & A & B \\ SG + LC & B & B & A & B & C & B & D & D \\ SG + MC & B & B & A & C & C & B & D & D \\ SG + DT & B & B & A & C & B & A & B & B \\ SG + SNV + DT & B & A & D & C & A & A & A \\ SG + SNV + FD & B & B & B & C & C & C & B & A \\ SG + SNV + SD & C & D & D & D & D & D & B & B \\ SG + MSC + FD & B & B & B & D & D & D & B & A \\ SG + MSC + SD & D & D & D & D & D & B & B \\ SG + LG + FD & B & B & B & D & C & D & A & A \\ SG + LG + SD & B & D & D & D & D & A & A \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RPD level of regression models with different pretreatment methods. The nonlinear function was employed to train PLS and SVR. Therefore, the PLS models with linear, sigmoid, and RBF functions are respectively referred to as PLS_linear, PLS_sigmoid, and PLS_RBF, and the SVR models with linear, sigmoid, and RBF functions are respectively named SVR_linear, SVR_sigmoid, and SVR_RBF. RS indicates the VIS-NIR without any pretreatment. Thus, the results show that a few of the pretreatment datasets exhibit worse model performance than the RS dataset. In particular, the SVR algorithm has more models with \(R^{2}\) values lower than 0.2 compared with the PLS algorithm in Figure 4. Following the consideration of the pretreatment methods, the \(R^{2}\) value of PLS_linear with SG + MSC + SD was determined to be less than 0.2. However, \(R^{2}\) values of SVR linear with MSC + SD, SG + SD, SG + LG + FD and SG + LG + SD are less than 0.2, but the \(R^{2}\) value of PLS with a nonlinear function (sigmoid and RBF) is not only greater than 0.2 but also better than that of SVR. Therefore, PLS with a nonlinear function is more suitable for the regression prediction of VIS-NIR. In Figure 4, the \(R^{2}\) values of the boosting algorithms are all greater than 0.4, which means that the boosting regression algorithm is preferable to the other methods. In particular, the \(R^{2}\) of GBRT with the testing dataset is considerably greater than 0.5, and the \(R^{2}\) with the training dataset is close to 1.