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

30 November 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Scientific Machine Learning
Perfume Engineering
Scent Science
Scent Classification


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.