A step closer to digitizing the sense of smell: Model describes odors better than human panelists

 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 Center and start-up Osmo, a Cambridge, Mass.-based company spun out of machine learning research done at Google Research, Google DeepMind (formerly known as Google Brain), are investigating how airborne chemicals connect to odor perception in the brain. To this end they discovered that a machine-learning 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 Center Member. This collaboration moves the world closer to digitizing odors to be recorded and reproduced. It also may identify new odors for the fragrance and flavor 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 malodor 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 color 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 odors, 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 odor with the odor's molecular structure. The resulting map of these interactions is essentially groupings of similarly smelling odors, like floral sweet and candy sweet. "Computers have been able to digitize 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 odor perception."

What is the smell of garlic or of ozone?

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

Source: ScienceDaily