In Table we report the precision, recall, and F1 scores for the identification of the 9 different functional groups. The model exhibits high discriminative power, with F1 scores ranging from 0.83 to 0.95. Notably, alcohols achieve the highest F1 score (0.95) with near-perfect precision (0.99), indicating minimal false positives. Ethers and aldehydes follow closely (F1: 0.94), despite the significant disparity in their support sizes (594 vs. 60). This suggests effective learning even with class imbalance. Amides show the lowest F1-score (0.83), primarily due to a lower recall (0.79), indicating a higher false negative rate. The precision-recall trade-off is evident in groups like nitro (precision: 0.95, recall: 0.85) and amines (precision: 0.79, recall: 0.90), suggesting potential thresholding adjustments could optimize performance. The consistently high metrics across varied support sizes demonstrate the model's robustness to class distribution variations in spectral data classification. We also displayed the confusion matrix of j-IR-vis in Fig. , further corroborating the high accuracy for aldehydes, carboxylic acids, and esters. From Fig. we can observe that j-IR-vis has some challenges predicting ethers, as they are often misclassified as amines or esters, which aligns with the lower precision observed for amines. The confusion between amides and ketones is also apparent, explaining the lower F1 score for amides.