AI Predicts Whisky Origins And Aromas Better Than Humans

Scientists at the Fraunhofer Institute for Process Engineering and Packaging IVV in Germany, have developed an AI tool called OWSum that outperforms human experts in identifying the origin and key aromas of whisky samples.

Using molecular data and machine learning, the tool demonstrated exceptional accuracy in distinguishing Scotch whisky from American whiskey and predicting their aroma profiles, as outlined in the research paper published on Thursday.

The study analyzed 16 whisky samples using chemical data and human sensory evaluations. Researchers found that OWSum, a linear classifier, could predict whisky origins based on detected molecules with high precision.

For example, certain chemicals, like menthol and citronellol, were linked to American whiskey, while Scotch whisky had unique markers such as methyl decanoate.

To go further, the team used both OWSum and a Convolutional Neural Network (CNN) to predict each whisky’s top five aroma attributes. These included descriptors like fruity, woody, or smoky. The AI models performed better than human experts, achieving scores that showed strong consistency and accuracy.

Unlike humans, who often vary in their evaluations, AI tools provided consistent predictions. However, the study noted that neither model considered the concentration of molecules, which could improve results in the future.

Researchers believe such AI applications could be valuable in the whisky industry for quality control, fraud detection, and developing new blends. The tools might also extend to other industries, such as food and fragrance production, as reported by New Scientist.

Although AI outperformed humans in this study, scientists emphasize that human expertise is still essential for training these models and interpreting results. Future advancements may include refining aroma profiles and expanding analysis to other whisky-producing regions.

The study demonstrates how technology can complement traditional sensory methods, offering new insights into complex aroma compositions.

“[The results] underline the fact that it’s a complicated task for humans, but it’s also a complicated task for machines – but machines are more consistent than humans,”says Satnam Singh, a team member at the Fraunhofer Institute, as reported by New Scientist.

“But that’s not to say that humans are not needed: we do need them to train our machines, at least, right now,” he added.

The study focused on a small selection of whiskies, and it’s uncertain how the AI would perform with a larger variety, or how it would handle the flavor notes that evolve as the whisky ages in the cask.

The Guardian reports Dr. William Peveler, a senior lecturer in chemistry at the University of Glasgow, saying,

“The other thing with whisky is that perception of flavour is hugely influenced by the environment it’s consumed in and other external factors, so there could be some work to do on other factors that influence flavour perception and prediction in such an emotive product.”