In the literature, various methods for analysing features have been employed . Among these, Licon et al. introduced a database comprising 1689 odorants and proposed a computational method based on subgroup discovery algorithms to search rules connecting chemistry with perception . Lee et al. employed the same Graph Neural Network (GNN) to develop a scent map, analogous to colour maps, positioning molecules with comparable scents in proximity . Their findings further illustrated the GNN's ability to generalize across new datasets and various scent-prediction tasks. Furthermore, Liu et al. presented DeepSniffer, a framework aimed at classifying scents in essential oils using a Multilayer Perceptron (MLP) that is based on k-shot meta-learning . In order to create machine learning algorithms that can precisely predict sensory attributes, including odor intensity, pleasantness, and eight semantic descriptors for 480 structurally diverse odorant molecules, Keller and colleagues organized the crowdsourced DREAM Olfaction Prediction Challenge . To predict patients' perceptual ratings based on 4,884 molecular parameters, they trained 1,089 regression models using a random forest approach. The correlation between the observed and projected scores served as the performance metric . In parallel, Zhang and co-authors created predictive models based on Convolutional Neural Networks (CNNs) to analyze odor characteristics and pleasantness. Their objective was to develop a process for creating and evaluating fragrance molecules, each of which was to be represented by a distinct olfactory character .