One of our unique contributions lies in integrating uncertainty quantification with the odor classification model, leveraging the output probabilities of graph convolutional neural networks to assign weights to odor intensity probability distribution functions and then transforming samples into probabilities by counting dominance. This innovative method enhances the reliability of the predictions and gives a probabilistic estimate of the fragrance's dominant smell.