Notch up another win for artificial intelligence. Researchers have used the technology to predict the notes that waft off whisky and determine whether a dram was made in the US or Scotland.
The work is a step towards automated systems that can predict the complex aroma of whisky from its molecular makeup. Expert panels usually assess woody, smoky, buttery or caramel aromas, which can help to ensure they don’t vary substantially between batches of the same product.
“The beautiful thing about the AI is that it is very consistent,” said Dr Andreas Grasskamp, who led the research at the Fraunhofer Institute for Process Engineering and Packaging in Freising, Germany.
“You have this subjectivity still in trained experts. We are not replacing the human nose with this, but we are really supporting it through efficiency and consistency.”
Nailing down a whisky’s aroma is no simple business. Most of the strongest notes in the spirit are a complex mixture of chemicals that interact in the nose and even mask one another to create a particular aromatic impression. The interactions make it extremely difficult to predict how the whisky will smell from its chemical signature.
For the latest work, the researchers obtained the chemical makeup of 16 US whiskeys and Scottish whiskies, including Jack Daniel’s, Maker’s Mark, Laphroaig and Talisker, and details of their aromas from an 11-strong expert panel. The information was used to train AI algorithms to predict the five major aromas and origin of the drinks from their molecular constituents.
One algorithm was more than 90% accurate at distinguishing the US from Scottish spirits, though the performance would be likely to drop against tipples it had not been trained on. On average, it identified the five strongest notes in each whisky more accurately and consistently than any individual on the expert panel. The details have been published in Communications Chemistry.
The compounds menthol and citronellol helped to identify US whiskeys, which often have a caramel-like note. Methyl decanoate and heptanoic acid were important for identifying scotch, which often has a smoky or medicinal aroma.
The researchers see applications in areas beyond whisky, from detecting counterfeit products through discrepancies in their smell, to finding the best ways to blend old recycled plastics, which can develop unpleasant odours, into new products without the smell being noticeable.
Dr William Peveler, a senior lecturer in chemistry at the University of Glasgow, said the approach could provide more “stability” than a human taste panel. “The flavour notes of a whisky brand could be quickly checked from batch to batch or blend to blend based on the chemical signature alone, to try to ensure a consistent house style,” he said.
The study involved only a small number of whiskies and it is unclear how the AI would perform when faced with more, he added, and how it would deal with the flavour notes that developed with age in the cask. “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,” he said.
