PHILADELPHIA, PA – Aug. 20, 2026 – 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 to 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 work, creating a means of using machine learning to discriminate among scents and how they relate to each other. A description of the work was published online Aug. 4 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, Ph.D., 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 amount of similarity between two scent mixtures. First, they standardized and compiled six datasets of odor-similarity measurements from three different studies into a new dataset, 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).
Next, over a three-month period, 26 teams competed to predict how similar the paired scents would be on a hidden test-set of 46 mixture pairs. The competition resulted in a four-way tie. Mainland and colleagues then built an ensemble model by averaging the predictions from the four winning teams with those from two additional high-performing models. Following the challenge, they validated the model on an independent set of 50 olfactory mixture pairs.
Their final model was found to be quite accurate. It achieved a median RMSE (root mean squared error) of 0.08, with 0 being a perfect score, meaning the model had good accuracy in predicting scent similarities. It also achieved a Pearson correlation of 0.57 on the test set, meaning the model had a moderate to strong positive ability to predict the characteristics of the dataset.
While many in the sensory science field believed that using science to predict similarity among mixtures was going to be much more difficult than in single molecules, that didn't turn out to be the case, Mainland said: "This paper shows that if you're already able to predict what a single component smells like, then you can make a pretty good prediction of mixtures out of that."
Interestingly, he said, the machine learning models were more likely to use semantic language like "fruity and sweet" to describe scent mixtures rather than chemistry terms like ester or molecular weights. "Working with these semantic labels is a huge jump forward in being able to make predictions of what mixtures smell like," he said. When a journal reviewer asked them to eliminate the semantic features to see how the models performed without that information, it was much more difficult to make predictions.
Investigators are now writing up results from a third DREAM Olfaction challenge to submit for journal publication. In that exercise, teams were given people's impressions of what individual components of mixtures smelled like and were asked to use that information to predict how the complete mixture would smell.
"What we're seeing is that, again, if you know what the components smell like, you know what the mixture smells like — it's basically just an average of the components, which I think is really surprising to a lot of people in the field," he said.
Co-authors of the current study include Xuebo Song, Tiffany Yang and Robert Pellegrino of the Monell Center. Other contributing authors were from State University of Londrina in Brazil, Texas A&M University, Yale University, Boston University, the University of Michigan, KU Leuven in Belgium, Université Cote d'Azur in France, University of Oxford in the United Kingdom, KTH Royal Institute of Technology in Sweden, Cornell University, Cold Spring Harbor Laboratory, Sage Bionetworks, University of the Basque Country in Spain, the Basque Foundation for Science in Spain, the University of California at Davis, the University of Toronto in Canada, The Rockefeller University, the Weizmann Institute of Science in Israel, the University of Pennsylvania, and IBM Research.
The work was supported in part by grants from the National Institutes of Health (R01 DC017757), the NOMIS Foundation, Pershing Square Philanthropies, the Stavros Niarchos Foundation, Rothberg Catalyzer, Paul Graham Foundation, Schmidt Futures, ERC SynGrant 101118977 D2Smell, William R. Miller Fellowship, French National Research Agency (ANR-19-CE07-0044), Fondation Roudnitska, and the Initiative of Excellence Université Côte d'Azur (ANR-15-IDEX-01). Additional funders include the Howard Hughes Medical Institute, the College of LSA at the University of Michigan, and the Flemish government.
Mainland serves on the scientific advisory board of Osmo Labs, PBC, and receives compensation for these activities.
