## 3 Results
### Model Optimization Based on Feature Wavelength Selection  
To further enhance classification performance and reduce spectral dimensionality, three feature wavelength selection methods--successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS)--were applied to the CWT-preprocessed spectra. Together with the three classifiers (SVM, RF, and XGBoost), a total of nine feature-selected models were constructed. Additionally, three baseline models using the full CWT-preprocessed spectra without feature selection were also included (CWT-SVM, CWT-RF, CWT-XGBoost), bringing the total number of models in this comparison to twelve. Their classification performance was evaluated based on accuracy, precision, recall, and F1-score, as summarized in Table 2.  
Among all tested combinations, the UVE-CWT-SVM model achieved the highest overall performance, with accuracy of 93.22%, precision of 92.90%, recall of 93.58%, and F1-score of 93.12%. This demonstrates an excellent balance across all four metrics and confirms the strong synergy between UVE's variable stability filtering and SVM's robustness in small-sample, high-dimensional scenarios. UVE-CWT-RF and UVE-CWT-XGBoost also performed well, achieving accuracies of 91.53% and 89.83%, respectively. These results \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Classification Performance Metrics**} & \multirow{2}{*}{**F1-Score (\%)**} \\  & & **Precision (\%)** & & **Recall (\%)** \\ \hline CWT-SVM & 88.14 & 87.10 & 87.20 & 87.15 \\ CWT-RF & 88.63 & 86.70 & 88.10 & 87.39 \\ (Full Spectrum) & & & & \\ CWT-XGBoost & 87.83 & 88.60 & 89.30 & 88.94 \\ (Full Spectrum) & & & & \\ SPA-CWT-SVM & 86.44 & 84.50 & 85.20 & 84.85 \\ SPA-CWT-RF & 89.58 & 83.60 & 82.90 & 83.24 \\ SPA-CWT-XGBoost & 90.68 & 89.80 & 90.10 & 89.95 \\ UVE-CWT-SVM & 93.22 & 92.90 & 93.58 & 93.12 \\ UVE-CWT-RF & 91.53 & 89.70 & 91.80 & 90.74 \\ UVE-CWT-XGBoost & 89.83 & 88.90 & 89.30 & 89.10 \\ CARS-CWT-SVM & 81.36 & 79.10 & 80.50 & 79.79 \\ CARS-CWT-RF & 83.05 & 81.20 & 82.60 & 81.89 \\ CARS-CWT-XGBoost & 79.66 & 78.40 & 79.20 & 78.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification performance of models based on different feature wavelength selection methods. suggest that UVE effectively selects physiologically relevant and discriminative bands that are compatible with various classifier architectures. The SPA-based models also yielded competitive results. For example, SPA-CWT-XGBoost reached an accuracy of 90.68% and an F1-score of 90.10%, followed by SPA-CWT-RF (89.58%) and SPA-CWT-SVM (86.44%). However, compared to the UVE-based counterparts, the SPA models exhibited slightly lower recall and F1-score values, indicating reduced sensitivity in distinguishing intermediate Bruise levels, such as moderate versus severe damage. In contrast, CARS-based models consistently underperformed. The CARS-CWT-XGBoost model achieved only 79.66% accuracy and an F1-score of 78.79%, suggesting that its variable selection might have omitted key wavelengths or retained noisy bands, thereby weakening its classification performance. The three full-spectrum models (CWT-SVM, CWT-RF, and CWT-XGBoost) also demonstrated reasonably strong performance, with accuracies ranging from 87.83% to 88.63%. Notably, the best full-spectrum model (CWT-XGBoost, 88.94% F1-score) still lagged behind the UVE-CWT-SVM model, highlighting the benefit of informative feature selection over raw full-band input. These baseline comparisons reinforce the idea that appropriate wavelength selection not only reduces dimensionality but also improves generalization by mitigating the redundant or irrelevant spectral content.