A step closer to digitising the sense of smell

The Monell Chemical Senses Centre and startup Osmo are investigating how airborne chemicals connect to odour perception in the brain.

A main crux of neuroscience is learning how our senses translate light into sight, sound into hearing, food into taste, and texture into touch. Smell is where these sensory relationships get more complex and perplexing.

To address this question, a research team co-led by the Monell Chemical Senses Centre and startup Osmo, a Cambridge, Mass.-based company spun out of machine-learning research at Google Research, are investigating how airborne chemicals connect to odour perception in the brain. To this end they discovered that their model has achieved human-level proficiency at describing, in words, how chemicals might smell. Their research appears in the September 1 issue of Science.

“The model addresses age-old gaps in the scientific understanding of the sense of smell,” said senior co-author Joel Mainland, PhD, Monell Centre Member. This collaboration moves the world closer to digitising odours to be recorded and reproduced. It also may identify new odours for the fragrance and flavour industry that could not only decrease dependence on naturally sourced endangered plants, but also identify new functional scents for such uses as mosquito repellent or malodour masking.

How our brains and noses work together

Humans have about 400 functional olfactory receptors. These are proteins at the end of olfactory nerves that connect with airborne molecules to transmit an electrical signal to the olfactory bulb. The number of olfactory receptors is much more than we use for colour vision – four – or even taste – about 40.

“In olfaction research, however, the question of what physical properties make an airborne molecule smell the way it does to the brain has remained an enigma,” said Mainland. “But if a computer can discern the relationship between how molecules are shaped and how we ultimately perceive their odours, scientists could use that knowledge to advance the understanding of how our brains and noses work together.”

To address this, Osmo CEO Alex Wiltschko, PhD and his team created a model that learned how to match the prose descriptions of a molecule’s odour with the odour’s molecular structure. The resulting map of these interactions is essentially groupings of similarly smelling odours, like floral sweet and candy sweet.

“Computers have been able to digitise vision and hearing, but not smell – our deepest and oldest sense,” said Wiltschko. “This study proposes and validates a novel data-driven map of human olfaction, matching chemical structure to odour perception.”

What is the smell of garlic or of ozone?

The model was trained using an industry dataset that included the molecular structures and odour qualities of 5,000 known odorants. Data input is the shape of a molecule, and the output is a prediction of which odour words best describe its smell.

To ascertain the efficacy of the model, researchers at Monell conducted a blind validation procedure in which a panel of trained research participants described new molecules, and then compared their answers with the model’s description. The 15 panellists were each given 400 odorants as well as trained to use a set of 55 words – from mint to musty – to describe each molecule.

“Our confidence in this model can only be as good as our confidence in the data we used to test it,” said co-first author Emily Mayhew, PhD, who conducted this research while a Monell postdoctoral fellow. She is now an assistant professor at Michigan State University. Brian K. Lee, PhD, Google Research, Cambridge, Mass., is also a co-first author.

The Monell team supplied panellists with lab-designed odour reference kits to teach them how to recognise the smells and select the most appropriate words to describe their perception. To avoid pitfalls from past studies like panellist conflation of ‘musty,’ like a wet basement, and ‘musky,’ like a perfume, training sessions and lab-designed odour reference kits taught each panellist the odour quality associated with each descriptive term.

The panellists were asked to select which of the 55 descriptors applied and to rate the extent to which the term best applied to the odour on a 1-to-5 scale for each of the 400 odours. For example, one panellist rated the smell of the previously uncharacterised odorant 2,3-dihydrobenzofuran-5-carboxaldehyde as very powdery (5) and somewhat sweet (3).

Quality control is also important in the final comparison of the human sniffers to the computer model. That’s where co-author Jane Parker, PhD, Professor of Flavour Chemistry, University of Reading, UK comes in. “I’ve worked on smell for many years, relying mainly on my own nose to describe aromas.” Her team verified the purity of samples used to test the model’s prediction. First, gas chromatography enabled them to separate out each compound in a sample, including any impurities. Next, Parker and her team smelled each separated compound to determine whether any impurity is overwhelming the target molecule’s known odour.

“We did find a few samples with significant impurities, among the 50 tested,” Parker said. In one case, the impurity was from traces of a reagent used in the synthesis of the target molecule and gave the sample a distinctive buttery smell that overpowered the odorant of interest. “In this case we were able to explain why the panel had described the smell differently to the AI prediction.”

Better than a human?

In comparing the model’s performance to that of individual panellists, the model achieved better predictions of the average of the group’s odour ratings than any single panellist in the study, impurities aside. Specifically, the model performed better than the average panellist for 53% of the molecules tested.

“The most surprising result, however, is that the model succeeded at olfactory tasks it was not trained to do,” said Mainland. “The eye-opener was that we never trained it to learn odour strength, but it could nonetheless make accurate predictions.”

The model was able to identify dozens of pairs of structurally dissimilar molecules that had counter-intuitively similar smells, and characterise a wide variety of odour properties, such as odour strength, for 500,000 potential scent molecules. “We hope this map will be useful to researchers in chemistry, olfactory neuroscience, and psychophysics as a new tool for investigating the nature of olfactory sensation,” said Mainland.

What’s next? The team surmises that the model map may be organised based on metabolism, which would be a fundamental shift in how scientists think about odours. In other words, odours that are close to each other on the map, or perceptually similar, are also more likely to be metabolically related. Sensory scientists currently organise molecules the way a chemist would, for example, asking does it have an ester or an aromatic ring?

“Our brains don’t organise odours in this way,” said Mainland. “Instead, this map suggests that our brains may organise odours according to the nutrients from which they derive.”