Journal article
Predicting Odor Pleasantness with an Electronic Nose
PLoS Computational Biology, Vol.6(4), e1000740
Apr/2010
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 naive 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.
Details
- Title
- Predicting Odor Pleasantness with an Electronic Nose
- Creators
- Rafi Haddad (null)Abebe Medhanie (null) - The Weizmann Institute of ScienceYehudah Roth (null)David Harel (null) - 972WIS_INST___83Noam Sobel (null) - 972WIS_INST___123
- Resource Type
- Journal article
- Publication Details
- PLoS Computational Biology, Vol.6(4), e1000740; Apr/2010
- Number of pages
- 11
- Language
- English
- DOI
- https://doi.org/10.1371/journal.pcbi.1000740
- Grant note
- European Research Council [200850]This work was supported by a grant from the European Research Council # 200850. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript._ALMAME_DELIMITER_
- Record Identifier
- 993262360703596
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