Artificial intelligence may not be able to appreciate the taste of a glass of red wine, but apparently it can tell you where it came from. And by doing so, modern technology might confirm the classic concept of terroir.
A team of scientists used machine learning and gas chromatography to successfully analyze 80 wines and identify which seven Bordeaux châteaus produced them. Even more astonishing, when they mapped the wines’ chemical signatures as XY coordinates on a map, the wines clustered correctly according to their Left and Right Bank origins. This breakthrough research validates the concept of terroir and has far-reaching implications.
“This study demonstrates the remarkable potential of gas chromatography analysis to explore fundamental questions about the origin and age of wine,” said the lead authors, Alexandre Pouget, a computational neuroscientist at the University of Geneva, and research enologist Stéphanie Marchand of the Institute of Vine and Wine Science (ISVV) at the Université de Bordeaux. They published their findings in the journal Communications Chemistry.
Combining Wine Passion With Science Know-How
The impetus behind the project was a question that had plagued Pouget for decades: Could he use machine learning to study wine? Born and raised in Paris, he has had a lifetime passion for wine. As a young neuroscientist in the late 1980s, he studied the brain with machine learning, a type of artificial intelligence that identifies patterns in large data sets.
His work took him to California, New York and Geneva, but the question of whether he could use machine learning to analyze wine lingered—until six years ago when he and his wife visited Veuve Clicquot for a private tasting. He mentioned his idea, and the winemaking team pointed him in the direction of the ISVV in Bordeaux and to Marchand, who has specialized in the chemical analysis of wine since 2008. The project then got its start as a humble “Saturday experiment” in the ISVV’s chemistry lab, said Marchand.[article-img-container][src=2023-12/ns_bordeaux-chromatography-121923_1600.jpg] [credit= (BSIP/Universal Images Group via Getty Images)] [alt= A scientist operates a gas chromatography machine.][end: article-img-container]
The idea was to use gas chromatography (GC) and electron ionization mass spectrometry paired with the computational powers of machine learning to identify specific wines by their molecular signature. “In this particular paper we asked two main questions: Can we identify the estate, independent of vintage? And can we identify the vintage, independent of the estate?” said Pouget.
Gas chromatography has been used for years to analyze wine. It’s a straightforward process that vaporizes a chemical mixture, in this case, wine, and records the various molecular components in the form of a chromatogram. “The measurements look like an electrocardiogram with peaks but it’s not repetitive,” said Pouget. Each peak is made of multiple points, and when you get a peak, it means there is more of that substance. Further analysis is done to determine what the peaks are. “It’s a lot of work and for a variety of reasons, they need to manually calculate how big the peak is.”
As a neuroscientist, Pouget has used machine learning to identify patterns in large data sets related to the brain. Handling such large data translated to the chemical analysis of wine, because when a wine is put through GC, the resulting data set is vast—30,000 different points on the chromatogram.
Machine learning makes analysis much faster and easier as the algorithm is designed to know where to look in the data. It also removes the human tendency to second guess. Some of the peaks are tiny, so a researcher will make arbitrary decisions on which might be important. “But with machine learning the algorithm will go in there…
Source : https://www.winespectator.com/articles/artificial-intelligence-identifies-bordeaux-wine-terroir