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Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem

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  • Ritesh Kumar
  • Rishemjit Kaur
  • Benjamin Auffarth
  • Amol P Bhondekar

Abstract

Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules. We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces. We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC).

Suggested Citation

  • Ritesh Kumar & Rishemjit Kaur & Benjamin Auffarth & Amol P Bhondekar, 2015. "Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    • Handle: RePEc:plo:pone00:0141263
    DOI: 10.1371/journal.pone.0141263
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    References listed on IDEAS

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    1. Kobi Snitz & Adi Yablonka & Tali Weiss & Idan Frumin & Rehan M Khan & Noam Sobel, 2013. "Predicting Odor Perceptual Similarity from Odor Structure," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-12, September.
    2. Jason B Castro & Arvind Ramanathan & Chakra S Chennubhotla, 2013. "Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    3. Mark Stopfer & Gilles Laurent, 1999. "Short-term memory in olfactory network dynamics," Nature, Nature, vol. 402(6762), pages 664-668, December.
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