Moët Hennessy, Analog Devices and the University of California, Davis announced a joint effort to deploy AI-powered chemical signature detection technology aimed at enhancing wine quality and supporting the long-term vitality of vineyards. The collaboration builds on technology with roots in early research at the Massachusetts Institute of Technology, which Analog Devices continues to advance for analyzing chemical signatures across a broad range of applications. In a world first, researchers at Moët Hennessy's Robert-Jean de Vogüé Research Center used a unique library of samples and data to train machine learning algorithms that successfully identified samples at elevated risk of developing Fresh Mushroom Aroma well before the defect would traditionally be detected. The system, powered by Analog Devices' sensing and machine learning platform, continuously learns from complex chemical data rather than looking only for predefined conditions. Beyond Fresh Mushroom Aroma, the organizations are researching early detection of vine diseases, soil assessment, and other defects linked to climate change and related events such as wildfires, with Analog Devices intending to make the technology broadly available for future industrial applications.
Analog Devices IncAnalog Devices' sensing and machine learning platform powers the AI chemical signature detection deployed with Moët Hennessy and UC Davis, with plans to make it broadly available for industrial applications.
LVMH Moët Hennessy - Louis VuittonMoët Hennessy's research center used its sample library to train ML algorithms that detect wine defects like Fresh Mushroom Aroma early, enhancing wine quality and vineyard vitality.
