Advancing Odor Classification Models Enhanced by Scientific Machine Learning and Mechanistic Model: Probabilistic Weight Assignment for Odor Intensity Prediction and Uncertainty Analysis for Robust Fragrance Classification

This study presents an innovative framework for classifying and predicting odor intensity in perfumery, combining scientific machine learning with mechanistic modeling to enhance fragrance design precision. A probabilistic weight assignment is introduced, utilizing scent classifier outputs to determine the contribution of each fragrance component, thereby recognizing the subjective nature of scent classification and variability in olfactory perception. Additionally, an uncertainty analysis framework is integrated, quantifying uncertainties within perfume diffusion and human sensory perception models, thus improving model adaptability and reliability. The methodology comprises three parts: a perfume diffusion model that simulates fragrance molecule evaporation and dispersion, an odor perception model using Odor Value for scent intensity quantification, and an uncertainty quantification that rigorously analyzes model parameters and predictions. This approach aims to scientifically advance the art of perfumery, allowing for the creation of sophisticated fragrances with enhanced predictive accuracy.