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Computing Odor Images
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DOI：10.1021/acs.jafc.6b05573.png)
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Abstract
En 中文
This perspective examines psychophysical methods that may reveal the algorithms that encode odor images by integrating current data from sensory measurement into a computational model of odor perception. There is evidence that algorithms used by the nervous system to process odor sensations require input from only a few odorants, between three and eight. Furthermore, the number of recognizable odors in foods that contribute anything to the aroma of all foods is approximately 250. This may imply that it is the ratio of a small number of key odorants (KOs) that create a multitude of food odors. Studies with large mixtures of odorants (formulated to be of equal potency) show that a subject's ability to detect individual odorants in these mixtures was vanishingly small. These large mixtures had weak and nondescript but similar odor character. If only a few stimulants are used to represent complex images, it is direct evidence of the simplicity and therefore the tractability of the computational process.
Keywords:
sniff olfactometry
odor image
odorant mixtures
Laing limit
olfactory white
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