Moët Hennessy and ADI Deploy AI to Detect Wine Quality Risks

The joint initiative utilizes technology originally developed at MIT, now refined by Analog Devices to bridge the gap between physical chemical signals and digital analysis. By training machine learning algorithms on a vast library of samples from Moët Hennessy’s Robert-Jean de Vogüé Research Center, the team has successfully identified markers of Fresh Mushroom Aroma in the early stages of production. This represents a shift in winemaking from reactive correction to proactive risk management.

Beyond addressing specific aroma defects, the platform is designed to learn continuously from complex biological data. Researchers are already exploring future applications including the early detection of vine diseases, soil analysis, and environmental stressors linked to climate change. While the technology provides high-level data, the project partners emphasize that it is intended to augment, rather than replace, the judgment of human viticulturists and winemakers. By equipping experts with precise, actionable insights, the collaboration seeks to bolster the resilience of the wine industry while preserving traditional craftsmanship.