Predicting odor pleasantness with an electronic nose

Rafi Haddad, Abebe Medhanie, Yehudah Roth, David Harel, Noam Sobel

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

A primary goal for artificial nose (eNose) technology is to report perceptual qualities of novel odors. Currently, however, eNoses primarily detect and discriminate between odorants they previously "learned". We tuned an eNose to human odor pleasantness estimates. We then used the eNose to predict the pleasantness of novel odorants, and tested these predictions in naïve subjects who had not participated in the tuning procedure. We found that our apparatus generated odorant pleasantness ratings with above 80% similarity to average human ratings, and with above 90% accuracy at discriminating between categorically pleasant or unpleasant odorants. Similar results were obtained in two cultures, native Israeli and native Ethiopian, without retuning of the apparatus. These findings suggest that unlike in vision and audition, in olfaction there is a systematic predictable link between stimulus structure and stimulus pleasantness. This goes in contrast to the popular notion that odorant pleasantness is completely subjective, and may provide a new method for odor screening and environmental monitoring, as well as a critical building block for digital transmission of smell.

Original languageEnglish
JournalPLoS Computational Biology
Volume6
Issue number4
DOIs
StatePublished - 15 Apr 2010
Externally publishedYes

Funding

FundersFunder number
Seventh Framework Programme200850

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