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Related Concept Videos

Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
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Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase
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An algorithm for 353 odor detection thresholds in humans.

Michael H Abraham1, Ricardo Sánchez-Moreno, J Enrique Cometto-Muñiz

  • 1Department of Chemistry, University College London, London, UK. m.h.abraham@ucl.ac.uk

Chemical Senses
|October 7, 2011
PubMed
Summary

This study establishes a predictive model for odor detection thresholds (ODTs) using chemical structure. The model accurately estimates ODTs for various compounds, aiding in odor characterization and prediction.

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Area of Science:

  • Olfactory science
  • Computational chemistry
  • Chemical sensing

Background:

  • Odor detection thresholds (ODTs) are crucial for understanding olfactory perception.
  • Existing methods for ODT determination are labor-intensive and data can vary across studies.
  • A need exists for a standardized and predictive approach to ODT assessment.

Purpose of the Study:

  • To develop a linear equation correlating chemical structure with ODTs.
  • To establish a unified scale for ODT data from different research groups.
  • To enable prediction of ODTs for a wide range of chemical compounds.

Main Methods:

  • Linear regression analysis of 193 ODTs from Nagata's dataset using the Japanese triangular bag method.
  • Inclusion of indicator variables for chemical classes (aldehydes, acids, unsaturated esters, mercaptans) to account for potency variations.
  • Integration and scaling of ODT data from Cometto-Muñiz and Cain, Cometto-Muñiz and Abraham, and Hellman and Small datasets.

Main Results:

  • A linear equation was established for Nagata's data (R² = 0.748, SD = 0.830 log units).
  • Self-consistency of Nagata's data was estimated at 0.66 log units.
  • A unified linear equation for 353 ODTs was achieved with R² = 0.759 and SD = 0.819 log units.
  • Compound descriptors, calculable from structure, allow for prediction of ODTs on the unified scale.

Conclusions:

  • A robust quantitative structure-odor relationship (QSOR) model for predicting ODTs has been developed.
  • The model successfully harmonizes ODT data from multiple sources, creating a standardized scale.
  • This approach facilitates the prediction of odor potency for numerous volatile and semivolatile compounds based on their chemical structure.