A new approach that uses math to describe how smell is represented in the brain identifies 10 basic categories of odor.
Senses such as hearing and vision can be discussed in terms that most people understand and that are tied to measurable physical phenomena, researchers say.
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But the sense of smell, or olfaction, doesn’t lend itself to such a systematic understanding of what smells we perceive and how those perceptions relate to physical phenomena.
“It’s an open question how many fundamental types of odor qualities there are,” says Jason Castro, who began the current study as a postdoctoral fellow at the University of Pittsburgh and is now assistant professor of psychology and neuroscience at Bates College.
“This is in striking contrast to olfaction’s ‘sister sense’—taste—where we know that five basic qualities seem to organize sensations.”
Working with a standard set of olfactory perception data, Andrew Dravniek’s 1985 Atlas of Odor Character Profiles, Castro and colleagues applied a mathematical method called non-negative matrix factorization (NMF) to achieve “dimensionality reduction”—the simplification of information into coherent categories.
The process is similar to the way compressing a digital audio or image file reduces the file’s size without, ideally, compromising its usefulness.
“What NMF is good at,” says Chakra Chennubhotla, assistant professor of computational and systems biology, “is dividing a dataset into its constituent parts. You have to give hints for how many parts you may expect to find, but otherwise you let the data decide. NMF has been successfully used in many other areas including the financial world and the processing of still images and videos.”
From sweet to sickening
From the data, the team identified 10 basic odor qualities: sweet, fragrant, woody/resinous, fruity (non-citrus), chemical, minty/peppermint, popcorn, lemon, and two types of sickening—decaying and pungent.
An intriguing aspect of the work is that the different qualities seem to be associated with different chemical features, though Castro is quick to stress that more research is necessary on this front.
In ongoing work, the researchers are now approaching the problem from the other direction, applying the current research to a bank of chemical structures in an attempt to predict how a given chemical is going to smell.
“That’s something that nobody’s really done with any kind of compelling accuracy,” Castro says. “And obviously perfume companies, flavor and fragrance companies, are really interested in doing that well.”
Arvind Ramanathan, a computer scientist at Oak Ridge National Laboratory, contributed to the study, which was published in PLOS ONE.
Source: University of Pittsburgh
