“Did you ever try to measure a smell?” Alexander Graham Bell once asked an audience of graduands at a high school in Washington DC.
He then quizzed the probably confused class of 1914 as to whether they could tell when one scent was twice the strength of another, or measure the difference between two distinct odours. Eventually, though, he came to the point: “Until you can measure their likenesses and difference, you can have no science of odour,” Bell said. “If you are ambitious to find a new science, measure a smell.”
At the time, scientists had an understanding that the sound and sight of Bell speaking on the stage could be described in terms of vibrations in the air and different wavelengths of light, but there was no comparable way of explaining the odours in the air that day in May. The mechanics of smell were a mystery, and in many ways they still are. “Unlike sound or vision – where the wavelength and amplitude clearly map to perceptual properties like tone frequency, colour or intensity – the relationship between a chemical’s structure and the underlying perception is not understood in olfaction,” explains Douglas Storace, assistant professor of neuroscience at Florida State University.
“The first thing to remember is how little attention and work has occurred in olfaction versus other fields,” says Alex Wiltschko, chief executive of olfactory AI startup Osmo, as he recalls the hefty neural science textbook he was given as a PhD student. “I took callipers and measured the width of the paper that’s used to teach vision and hearing. It’s about three quarters of an inch for vision. It’s about a half an inch for hearing. It’s maybe 30 pages – a few millimetres – for smell.”
Osmo’s stated purpose is to “give computers a sense of smell”, because while we have learned to digitally encode sights and sounds, we have no way of doing so for scents. Wiltschko and others are trying to change that, and usher in a new era of olfactory science, by mapping how we perceive odours.
Osmo is working to determine the basic elements of odour. Photograph: Ben Hider/Osmo
The human nose is essentially a chemical detector. When we smell a cup of coffee, for instance, we are sniffing up the volatile organic compounds (VOCs) that it has released into the air. “These small VOCs bind to certain olfactory receptors, and this binding basically triggers an electric signal that goes to the brain,” explains Cecília Roque, an associate professor of chemistry at Portugal’s Nova School of Science and Technology.
There are good reasons to want to replicate that process with machines. Some VOCs – such as contaminants in food or carcinogens such as benzene – can be harmful and worth detecting before they reach our noses; others might point to dangers such as gas leaks or concealed explosives; and some can indicate other problems. If someone’s breath smells like freshly mown clover it may be a sign of liver failure, while sweat with an odour of freshly plucked feathers could suggest a case of rubella.
Researchers have been developing electronic noses to help us detect certain compounds since the early 1980s, but while some are being used in industry today, their applications are often limited. “Demonstrations so far have either been very large analytical instruments, or are very narrowly targeted, or have relatively weak selectivity,” says Jacob Rosenstein, an associate professor of engineering at Brown University, who in 2018 co-developed a low-cost e-nose called Trufflebot.
According to some, what olfactory technology needs is a way of mapping molecules’ structures to their perceived smells. “Some molecules look very similar structurally and smell very different, and some look very different but smell very similar,” says Joel Mainland, a professor at the Monell Chemical Senses Centre in Philadelphia. “You’re constantly trying to build a model to fix that problem.”
Odours combined in the correct ratios could create any scent, effectively allowing us to recreate a smell as a printer recreates a picture
“You can’t design anything of meaningful complexity without a specification,” adds Wiltschko. “You can’t build a digital camera without the red, green, blue colour model (RGB). You can’t build a microphone without a low to high frequency space. And so the map has to come before the engineering.”
Wiltschko and Mainland were both members of a research team that published a study on odour mapping earlier this year. The research began while Wiltschko was working at Google Research, and involved a form of artificial intelligence called a graph neural network (GNN), which was trained using two large datasets linking molecular structure to odour. One of them, the Leffingwell dataset, was compiled in the early 2000s and pairs 3,523 molecules with descriptions of their smells. Acetaldehyde ethyl phenylethyl acetal, to take an example, apparently smells leafy green and lilac-like.
The work resulted in a “principal odour map” – the olfactory equivalent of the colour palette you might use on a computer. “Anybody who’s looked at a map of colour in Photoshop knows intuitively what’s going on,” says Mainland, and just as the “colour space” in such a map helps us say that purple is closer to red than to green, the team’s odour map allowed them to locate scents in a kind of multi-dimensional “smell space”.
“RGB is three-dimensional, but you can depict it on a flat piece of paper,” Wiltschko says. “There’s three channels of colour information in our eye, but there’s 350 channels of odour information in our nose.
Master perfumer Christophe Laudamiel is helping to categorise individual scent molecules. Photograph: Osmo
“Whatever map we were going to find was not going to fit on a flat piece of paper. Therefore, the map-making tools we’ve used as scientists in the past were not going to help us. We needed to wait for software, for artificial intelligence, for statistical analysis of patterns in large datasets.”
Now those technologies have arrived, they are not only allowing researchers to map the relationship between smells and their chemical structures, but predict them. For the study, the group trained a panel of 15 people to describe scents by rating them against 55 labels, including “buttery”, “earthy”, “sulphurous” and “metallic”, then asked them to apply these to 400 different molecules whose odours the GNN odour map had already predicted. The sample molecules were then passed to Christophe Laudamiel – a master perfumer now working with Osmo – for a more nuanced opinion. Mainland’s favourite of Laudamiel’s assessments, for a molecule that scored highly for descriptors such as musty, ozone, and medicinal, was: “the hot tub is near”.
“Some other ones are really interesting combinations,” Laudamiel adds. “One for instance smells very nice, of saffron and hot metal.”
Impressively, the GNN’s odour predictions for the 400 molecules turned out to be closer to the average human description more than 50% of the time. “Basically if you were to take that panel of people and pull one person out and put the model in its place, would you do better or worse at describing this average human perception?” says Mainland. “The answer here for most of the molecules, most of the time, is that it does better.”
The team went on to have the model predict odours for 500,000 additional molecules without needing to synthesise them first, and the work is continuing at Osmo. “Right now, they’re studying 7bn molecules,” Laudamiel says. “If I or you would spend just five minutes per ingredient to smell and study it, five minutes for 7bn molecules, it means you need 66,590 years.”
Having accurate predictions of the odours of so many previously unsmelled compounds would be a boon to those in the flavour and fragrance industries – Laudamiel likens it to having a piano that suddenly gains more keys – and this research is likely to have its biggest initial impact on the search for cheaper, safer, and more appealing scents in perfumes, laundry detergents, and anything else with added odour or flavour. But researchers hope the work can go much further than that. “If you think about what digitising images or digitising sounds has done for us, it’s not a thing that you can say very easily in one sentence, right?” says Mainland.
Wiltschko claims that agriculture, food storage, pandemic tracking and disease prevention would all benefit from our digitising smell, and some progress has already been made. Deet, or N,N-Diethyl-m-toluamide, is the oldest and most common insect repellent on the market, but it eats into clothes and plastics, can have adverse side-effects, and there’s evidence that some disease-causing mosquitoes may be developing resistance, becoming less sensitive to Deet’s smell. “We’ve actually published a paper showing we can find molecules that are as potent as Deet in human trials,” says Wiltschko.
For Mainland, one of the most exciting aspects of the research is the possibility of discovering “primary odours”. Just as red, green, and blue can be combined to create any hue, he hopes a finite set of odours combined in the correct ratios could create any scent, effectively allowing us to recreate a smell as a printer recreates a picture. Not only would the discovery of primary odours mean we could easily recreate any scent our noses are capable of smelling, they could even breathe new life into novelties such as the 1950s cinema format Smell-O-Vision. “It’s very exciting,” Laudamiel says. “We don’t necessarily know that they exist, but it’s very cool if they do.”
Before any of that is possible, though, researchers will need to map odours not just to individual compounds, but elaborate combinations that reflect the complexity of everyday odours. “Think of a smell that smells of only one thing,” Laudamiel points out. “People say, ‘oh, cut grass’. OK. Next time you go and you smell cut grass, whether on the ground or as you’re mowing the lawn, I guarantee you, it’s going to be grassy. It’s going to be mushroomy. It’s going to be earthy. It’s going to be maybe mouldy or musty or appley.”
Another issue, and one common to a lot of deep-learning AI models, is that this is essentially a black box. While the results are impressive and potentially useful, they don’t necessarily bring us closer to understanding the biological workings of smell. “Though there are connections, the relationship between chemical structure and qualitative olfactory perception is not directly linked,” says Rachel Herz, of the department of psychiatry and human behaviour at Brown University. “The human level is influenced by a multitude of variables ranging from experience, context, and language to individual differences in the genetic expression of olfactory receptors.
Ultimately, this may be just one small step towards understanding olfaction, but more than 100 years after Alexander Graham Bell asked whether we can measure the difference between two odours, the answer now appears to be “yes”.