## 3 Results
### Overview
The high AUC score of 0.8136 indicates strong discriminative power to distinguish between 'normal-to-high' and 'low' flavonoid content levels. This suggests that our model can effectively separate the two classes, which is crucial for accurate classification in practical applications. The relatively low standard deviation of the AUC (0.0159) suggests consistent performance across different repetition experiments. This consistency is important as it indicates that our model's performance is robust and not overly sensitive to the specific data split used in each fold of the cross-validation. The accuracy measure (ACC) provides additional insight into the model's overall classification performance. While the ACC is also high at 0.7461, it shows more variability (standard deviation of 0.0326) compared to the AUC metric. This higher variability in ACC could be due to factors such as class imbalance or the specific threshold chosen for classification. These results demonstrate the effectiveness of our approach in classifying flavonoid content levels. The combination of the ResNet1D architecture with wavelet transformation and Gabor-like filtering for feature extraction appears to be a powerful method for handling the complexities of hyperspectral data in this context. The high AUC and ACC scores suggest that this approach could be valuable in practical applications where accurate classification of flavonoid content is crucial. To evaluate the independent contributions of each feature extraction methods, we conducted systematic ablation studies comparing the performance of different feature combinations. In the first experiment, we removed Gabor-like filtering features while retaining wavelet transformation. Interestingly, this modification improved model performance, yielding an AUC of 0.8246 \(\pm\) 0.0180 and ACC of 0.7702 \(\pm\) 0.0188. Conversely, when we isolated Gabor-like filtering features by removing wavelet transformation, the model's performance decreased substantially, achieving an AUC of 0.6869 \(\pm\) 0.0531 and ACC of 0.6948 \(\pm\) 0.0440. These results reveal distinct contributions from each feature extraction method. Removing Gabor-like filtering features led to an improvement in AUC (from 0.8136 to 0.8246) and a high improvement in ACC (from 0.7461 to 0.7702). This suggests that Gabor-like filtering features may introduce noise that slightly impairs the model's discriminative capabilities. In contrast, discarding wavelet transformation features resulted in a more substantial decrease in both AUC (from 0.8136 to 0.6869) and ACC (from 0.7461 to 0.6948), indicating that wavelet transformation plays a crucial role in generating discriminative features for flavonoid content classification. These findings demonstrate that wavelet transformation alone provides optimal performance in our classification task. While the combination of both methods yields competent results, it performs slightly more poorly than wavelet transformation alone. Using Gabor-like filtering in isolation leads to notably lower performance. These results demonstrate that wavelet transformation is the most effective feature extraction technique for flavonoid content level classification. The performances are summarised in Table 1.  
To further investigate the importance of both wavelet transformation and Gabor-like filtering, we evaluated the ResNet1D architecture using hyperspectral data without any feature transformation methods. Using this data, the model demonstrated significantly lower performance, with an AUC of 0.6484 \(\pm\) 0.0353 and ACC of 0.6369 \(\pm\) 0.0388. This marked decrease in performance compared to our original results (AUC: 0.8136 \(\pm\) 0.0159,ACC: 0.7461 \(\pm\) 0.0326) demonstrates the crucial role of feature extraction in enhancing the model's classification capabilities. Without these transformations, the model's ability to discern meaningful patterns in the complex hyperspectral data is substantially reduced.