Predicting odor perceptual similarity from odor structure

PLoS Comput Biol. 2013;9(9):e1003184. doi: 10.1371/journal.pcbi.1003184. Epub 2013 Sep 12.

Abstract

To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain / physiology
  • Humans
  • Odorants*
  • Perception*
  • Sensory Thresholds*

Grant support

This study was funded by the James S McDonnel Foundation, a grant from the Israeli ministry of Science Israel-Japan program, and by the Adelis Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.