# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 3 Results and Discussion
### Model Performance Evaluation  
#### 3.3.1 Classification Performance  
After training, we saved the model that performed best on the validation set and evaluated its performance on the test set using classification metrics. The performance of the proposed framework was compared with that of traditional transformer, SVM, and KNN models. Figure 10 presents the confusion matrices for the four models on the test set without data preprocessing. As shown in Figure 10a, the proposed framework achieved the best classification performance, with only two misclassified samples, where two Mobil 2000 5W-40 samples were misclassified as Mobil Super 4T 20W-50. From the spectral graphs in Figure 6, it is evident that these two oils have similar spectra, and the dynamic measurement process introduces fluctuations in spectral intensity. Therefore, we consider this misclassification within the acceptable error range of the model. Similarly, the transformer model also demonstrated a classification performance comparable to the Hybrid framework. Considering the complexity of the fluorescence dataset, especially in terms of thickness variations, and the similarity between oil spectra, it is reasonable that the transformer model, which has been well-validated in large-scale language tasks, performs similarly to the proposed model. In contrast, the SVM and KNN models performed significantly worse than the deep learning models on the unprocessed dataset. Both SVM and KNN exhibited notable errors in identifying Mobil Super 4T 20W-50 and Mobil 2000 5W-40 samples. This indicates that traditional machine learning models struggle to accurately identify spectral features in the presence of complex interference signals. Next, the balance of the models' performance on the training and test sets was examined. As shown in Table 2, the two deep learning-based models performed similarly, with the proposed Hybrid framework achieving slightly better results. For SVM and KNN, their classification performance on the test set was better than on the training set. This may be due to the broader distribution and higher complexity of the augmented training data compared to the test set. As a result, even though these models struggled with the training set, the learned distributions aligned more closely with the test set. This outcome further demonstrates the effectiveness of the data augmentation method. Figure 10: (**a**) Confusion matrix on the test set for the Hybrid framework; (**b**) transformer; (**c**) SVM; (**d**) KNN. For traditional spectral analysis methods, data preprocessing is necessary to filter out interference signals. However, different preprocessing methods affect model performance in various ways, making preprocessing an additional hyperparameter for oil spill spectral analysis models. If a model can filter out interference signals during feature extraction without relying on preprocessing, it would have faster response times and greater practicality. Table 3 shows the classification performance of four models on the test set under no preprocessing and three preprocessing combinations. As shown in Table 3, the proposed Hybrid framework demonstrates consistent performance across different preprocessing methods, while the transformer model significantly improves its classification performance after SG filtering and normalization. This aligns with the transformer's sensitivity to noise. After SG filtering, noise in the spectra is significantly reduced, resulting in improved transformer performance. In contrast, the proposed Hybrid framework, due to the effective integration of the WaveConv block, sLSTM block, and KET block, exhibits reduced sensitivity to external interference and enhanced feature extraction and recognition capabilities. For KNN and SVM, their performance fluctuates under different preprocessing methods, further demonstrating the uncertain impact of preprocessing on spectral models.