A quantitative framework for predicting odor intensity across molecule and mixtures

Abstract In vision and hearing, standardized units such as lumens (for brightness) and decibels (for loudness) allow consistent quantification of stimulus intensity, enabling precise control of sensory experiences. Olfaction, by contrast, currently lacks a robust quantitative framework linking physical stimulus properties directly to perceived odor intensity, complicating efforts to accurately characterize and manipulate aromas. To bridge this gap, we used a precisely controlled odor delivery system combined with deep learning models to predict the intensity of both single molecules and mixtures from physical properties. These models allowed us to develop an automated, quantitative method that accurately identifies which volatile components meaningfully contribute to aroma perception, overcoming the limitations of traditional heuristic approaches such as odor activity values and demonstrating practical utility in complex naturalistic odors. Competing Interest Statement JM has received research funding from Ajinomoto Co., Inc. JM serves on the Scientific Advisory Board of Osmo Labs, PBC and receives compensation for this role. RG is an employee of Osmo Labs, PBC. YI is an employee of Ajinomoto Co., Inc.