As a result, our work advances the fragrance science field and contributes to the broader domain of probabilistic modeling in sensory perception. Future research could explore refinements to the methodology, such as incorporating additional sources of uncertainty (either epistemic or aleatoric), extending its applicability to other complex odorant mixtures, and testing the predictive capability against experimental data. Ultimately, the probabilistic approach presented here empowers fragrance designers with a data-driven framework to create perfumes that closely align with desired olfactory profiles, marking a significant step forward in the science of fragrance design.