AI Beats Human Experts At Distinguishing American Whiskey From Scotch
An AI system has outperformed human experts in distinguishing between American whiskey and Scotch, achieving 100% accuracy by identifying subtle differences in the chemical composition of the spirits. New Scientist reports: Andreas Grasskamp at the Fraunhofer Institute for Process Engineering and Packaging IVV in Germany and his colleagues trained an AI molecular odor prediction algorithm called OWSum on descriptions of different whiskies. Then, in a study involving 16 samples — nine types of Scotch whisky and seven types of American bourbon or whiskey — they tasked OWSum with telling drinks from the two nations apart based on keyword descriptions of their flavors, such as flowery, fruity, woody or smoky. Using these alone, the AI could tell which country a drink came from with almost 94 per cent accuracy.
Because the complex aroma of these spirits is determined by the absence or presence of many chemical compounds, the researchers also fed the AI a reference dataset of 390 molecules commonly found in whiskies. When they gave the AI data from gas chromatography — mass spectrometry showing which molecules were present in the sample spirits, it boosted OWSum’s ability to differentiate American from Scotch drams to 100 percent. Compounds such as menthol and citronellol were a dead giveaway for American whiskey, while the presence of methyl decanoate and heptanoic acid pointed to Scotch.
The researchers also tested both OWSum and a neural network on their ability to predict the top five odor keywords based on the chemical contents of a whisky. On a score from 1 for perfect accuracy to 0 for consistent inaccuracy, OWSum achieved 0.72. The neural network achieved 0.78 and human whisky expert test participants achieved only 0.57. The study has been published in the journal Nature Communications Chemistry.
Read more of this story at Slashdot.
An AI system has outperformed human experts in distinguishing between American whiskey and Scotch, achieving 100% accuracy by identifying subtle differences in the chemical composition of the spirits. New Scientist reports: Andreas Grasskamp at the Fraunhofer Institute for Process Engineering and Packaging IVV in Germany and his colleagues trained an AI molecular odor prediction algorithm called OWSum on descriptions of different whiskies. Then, in a study involving 16 samples — nine types of Scotch whisky and seven types of American bourbon or whiskey — they tasked OWSum with telling drinks from the two nations apart based on keyword descriptions of their flavors, such as flowery, fruity, woody or smoky. Using these alone, the AI could tell which country a drink came from with almost 94 per cent accuracy.
Because the complex aroma of these spirits is determined by the absence or presence of many chemical compounds, the researchers also fed the AI a reference dataset of 390 molecules commonly found in whiskies. When they gave the AI data from gas chromatography — mass spectrometry showing which molecules were present in the sample spirits, it boosted OWSum’s ability to differentiate American from Scotch drams to 100 percent. Compounds such as menthol and citronellol were a dead giveaway for American whiskey, while the presence of methyl decanoate and heptanoic acid pointed to Scotch.
The researchers also tested both OWSum and a neural network on their ability to predict the top five odor keywords based on the chemical contents of a whisky. On a score from 1 for perfect accuracy to 0 for consistent inaccuracy, OWSum achieved 0.72. The neural network achieved 0.78 and human whisky expert test participants achieved only 0.57. The study has been published in the journal Nature Communications Chemistry.
Read more of this story at Slashdot.