This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: If you want to describe a particular color, you could look to the Pantone color wheel to find its exact hue, saturation and brightness, and how it compares with other colors. But nothing like that has existed for complex odors.
Now, research co-authored by scientists at the Monell Chemical Senses Center has gotten closer to that goal by creating a means of using machine learning to distinguish among scents and how they relate to one another. A description of the work was published in the Proceedings of the National Academy of Sciences. The work provides a validated metric and benchmark for comparing smells, laying a foundation for technologies such as digital olfaction, the ability to digitize scents, said study co-author Joel Mainland, a member of the Monell Center.
"We've been interested for a long time in trying to digitize odors, to mathematically represent these in some way similar to what we have done with color vision and for hearing," Mainland said. In 2015, IBM ran an open investigator DREAM challenge to take a single odor molecule and predict what it smells like based on its chemical structure, he said. That drove a lot of science.
But most odors we encounter in day-to-day life are complex mixtures of dozens or hundreds of molecules. "We have to understand how mixtures work if we want to digitize anything," Mainland said. Being able to quantitatively map odor mixtures has numerous applications, he continued.
For example, some conditions like diabetes and liver failure have olfactory signatures, distinct scents that could be helpful in diagnosis. Mapping scents also could be helpful in quantifying flavors of foods or trademarking particular aromas like the scents of brand-name laundry detergents. "The companies that make smells are doing a lot of trial and error, so the thought process is that if you could fix that part where it's more mathematical, they could make products more efficiently," he said.
Mainland and colleagues released their own DREAM challenge, inviting international teams to use machine learning to develop models to predict the degree of similarity between two scent mixtures. First, they standardized and compiled six data sets of odor-similarity measurements from three different studies into a new data set, comprising 168 unique single molecules, 731 unique mixtures and 507 mixture-pair measurements. The mixture-pair distances were mapped onto a continuous perceptual scale from 0 (indistinguishable) to 1 (most distinct).
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