## 4 Results and Discussion
### Data Preprocessing Results
#### 4.1.3 Spectral Dimensionality Reduction Results  
After completing the spectral preprocessing, additional spectral dimensionality reduction was performed. In this experiment, the SPA and CARS feature selection methods, and the PCA and LLE feature extraction methods were used [27]. Due to the random nature of the results of both feature selection methods [34], the experiment is repeated five times for feature selection, and the minimum RMSECV is taken as the final result. Using larch as an example, Figures 10 and 11 display the results of SPA and CARS feature selection. In Figure 10a, RMSE on the cross-validation set showed a trend of decreasing first, then increasing, and then finally stabilizing with the increase of the wavelength variable. The optimal number of variables was finally selected as 10, whose selection position was shown in Figure 10b. CARS algorithm is a method based on Monte Carlo sampling. As shown in Figure 11, RMSE on the cross verification set showed a trend of decreasing first and then increasing with the increase of Monte Carlo iterations. The optimal number of iterations was finally selected as 19, and the number of selected wavelength variables was 66. The number of principal components retained after feature extraction by PCA, and CARS-LLE method was 10 and 6, respectively. The cumulative variance ratio of the first two principal components retained by PCA and CARS-LLE method was 78.69% and 87.767%, respectively. Figure 12 shows the distribution of principal component scores after PCA and CARS-LLE have extracted features. As shown in Figure 12, PCA, as a linear extraction method, extracts spectral features independently of each other, whereas LLE maintains the local linear features of the sample while downscaling. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Pretreatment Method**} & \multicolumn{2}{c}{**Cross-Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-5}  & **RMSECV: MPa** & **RCV** & **RMSEP: MPa** & **RP** \\ \hline Original spectrum & 3.6918 & 0.5594 & 3.9972 & 0.5157 \\ D2 + WT + MSC + VN & 6.6329 & 0.0318 & 6.2443 & 0.1465 \\ WT + MSC + VN & 2.4617 & 0.7354 & 2.4609 & 0.7359 \\ D1 + WT + MSC & 5.1621 & 0.4291 & 5.6343 & 0.3716 \\
**WT + MSC** & **2.2640** & **0.7550** & **2.3660** & **0.7453** \\ WT + SNV & 2.3657 & 0.7455 & 2.4513 & 0.7373 \\ WT & 3.2146 & 0.6294 & 3.3429 & 0.6174 \\ SG & 3.2519 & 0.6289 & 3.3876 & 0.6162 \\ SG + MSC & 2.5960 & 0.7278 & 2.5145 & 0.7317 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results of spectral preprocessing methods for larch. Figure 13 depicts the learning curve plots of the training samples before and after spectral dimensionality reduction. Figure 13 shows that the decision coefficient scores of the cross-validation set after dimensionality reduction are higher than those before dimensionality reduction, and that the scores of the training and validation sets tend to remain stable as the number of training samples increases. Figure 14 depicts validation curve plots of PLS principal component number before and after spectral dimensionality reduction. It can be seen that the optimal principal component number of the model is 3, and that increasing the principal component number further will result in overfitting. The comparison of the prediction results of each method is shown in Table 5.  
Figure 11: CARS feature selection results. Figure 12: Distribution of principal component scores: (**a**) PCA (PC1: Variance = 60.613%, PC2: Variance = 18.077%); (**b**) CARS-LLE (PC1: Variance = 67.685%, PC2: Variance = 20.082%). Figure 10: SPA feature selection results: (**a**) Optimal number of variables; (**b**) Location of feature. As shown in Table 5, all methods are based on WT + MSC spectrum pretreatment. The modeling effect of the preprocessed spectrum is improved to a certain extent after dimensionality reduction. Wavelength selection methods (SPA, CARS) and feature extraction methods (PCA, LLE) are not very different when used alone.