Predicting Odor Pleasantness with an Electronic Nose

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

PLOS Computational Biology, 2010, vol. 6, issue 4, 1-11

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.Author Summary: Electronic noses (eNoses) are devices aimed at mimicking animal noses. Typically, these devices contain a set of sensors that generate a pattern representing an odor. Application of eNoses entails first “training” the eNose to a particular odor, and once the eNose has “learned”, it can then be used to detect and identify this odor. Using this approach, eNoses have been tested in applications ranging from disease diagnosis to space-ship interior environmental monitoring. However, in contrast to animal noses, eNoses have not been used to generate information on novel odors they hadn't learned. Here, rather than train an eNose on particular odorants, we trained an eNose to the perceptual axis of odorant pleasantness. We found that this eNose was then able to generalize and rate the pleasantness of novel odors it never smelled before, and that these ratings were about 80% similar to those of naïve human raters who had not participated in the eNose training phase. Furthermore, the results replicated across cultures without retraining of the device. This result contrasts the popular notion that odorant pleasantness is completely subjective, and may allow for numerous applications, such as an environmental monitor that would warn of malodor regardless of its source.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1000740

DOI: 10.1371/journal.pcbi.1000740

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