We conducted an exploratory analysis focusing on feature fusion, which involved combining three types of features through simple concatenation methods. The resulting predictive performance of these fused features is outlined in Table , where the micro-averaged F1-scores are displayed. We used the best-performing model CSMLP with an 80-20 dataset split. Table clearly demonstrates that concatenating different modalities captures important features, leading to noticeable improvements in the results. Also, in future studies, a more advanced feature fusion strategy can be utilized for better performance. based on the presence of the predominant odor classes in them. Consequently, their averaged spectra provide a comprehensive illustration of how the best features are justified. Features with higher weights were marked red and gradually transitioning to blue gradients as weight decreased. These plots illustrate that the regions of interest for the model coincide with those containing the most significant physical information. This observation underscores the model's capability to accurately identify crucial regions within the spectra, thereby eliminating the concerns of random results or hallucinations.