## 3 Results
### Classification Results Based on Preprocessing and Modeling Methods  
To evaluate the impact of different spectral preprocessing techniques on classification performance, six datasets were constructed, i.e., raw spectra (Raw) and five preprocessed versions using MSC, SG, detrending, SNV, and continuous wavelet transform (CWT). Each dataset was modeled using three classifiers: SVM, RF, and XGBoost. The classification results based on accuracy, precision, recall, and F1-score are presented in Table 1.  
In terms of overall classification accuracy, the models employing CWT preprocessing consistently outperformed those using other methods across all three classifiers. Among them, the CWT-XGBoost model achieved the highest accuracy (89.83%), followed by CWT-SVM and CWT-RF, both reaching 88.14%. Meanwhile, these CWT-based combinations also maintained high scores in precision, recall, and F1-score, indicating that CWT effectively enhances spectral feature expression and improves the model's ability to distinguish between different levels of cumulative bruise damage. This multiscale transformation method is particularly well suited for the progressive damage classification scenario addressed in this study. By contrast, conventional scatter correction techniques such as MSC and SNV yielded lower accuracy in certain classifiers. For example, the MSC-RF and SNV-XGBoost models achieved only 67.80% and 66.10% accuracy, respectively, with F1-scores below 70%. These results suggest that such methods may have limited effectiveness in handling spectra from cumulative damage scenarios, possibly due to their weaker ability to suppress noise or enhance discriminative features. Additionally, the SG and detrending preprocessing methods showed highly variable performance across models, with classification accuracies ranging from 72.88% to 84.75%, reflecting less stability and generalizability under different modeling strategies. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Classification Performance Metrics**} \\  & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Raw–SVM & 83.05 & 81.20 & 82.50 & 81.84 \\ Raw–RF & 77.19 & 75.80 & 76.90 & 76.34 \\ Raw–XGBoost & 76.27 & 74.50 & 75.60 & 75.04 \\ MSC-SVM & 86.44 & 85.30 & 86.00 & 85.65 \\ MSC-RF & 67.80 & 66.20 & 67.50 & 66.84 \\ MSC-XGBoost & 67.80 & 66.40 & 67.20 & 66.80 \\ S-G-SVM & 84.75 & 83.50 & 84.00 & 83.74 \\ S-G-RF & 72.88 & 71.40 & 72.50 & 71.94 \\ S-G-XGBoost & 74.58 & 73.10 & 74.00 & 73.54 \\ Detrending–SVM & 84.75 & 83.60 & 84.10 & 83.84 \\ Detrending–RF & 74.58 & 73.20 & 74.10 & 73.64 \\ Detrending–XGBoost & 72.88 & 71.50 & 72.30 & 71.94 \\ SNV-SVM & 84.75 & 83.40 & 84.20 & 83.80 \\ SNV-RF & 69.49 & 68.20 & 69.10 & 68.64 \\ SNV-XGBoost & 66.10 & 65.00 & 66.00 & 65.50 \\ CWT-SVM & 88.14 & 87.00 & 87.80 & 87.39 \\ CWT-RF & 88.14 & 87.10 & 87.90 & 87.49 \\ CWT-XGBoost & 89.83 & 88.60 & 89.30 & 88.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification performance comparison of preprocessing–model combinations based on accuracy, precision, recall, and F1-score. Furthermore, raw spectra without any preprocessing (Raw) showed relatively good performance when used with SVM (accuracy: 83.05%) but significantly lower results with RF and XGBoost (accuracy, 77.19% and 76.27%, respectively). This further confirms that spectral redundancy and noise can hinder the construction of robust classification models when left unaddressed. In summary, CWT demonstrated strong capability in retaining both local and global spectral features through its multiscale decomposition mechanism, which makes it highly effective for detecting cumulative mechanical damage in prunes. Based on these findings, CWT was selected as the standard preprocessing method for subsequent feature selection and model construction to ensure optimal classification performance and model stability.