Currently, analytical tests based on gas-chromatography–mass-spectrometry (GC-MS) are too slow for harvest decisions. It is also difficult to determine the thresholds of smoke taint for each grape variety. Before harvest, week-long small-lot fermentation could estimate smoke exposure effects using sensory assessment but it was very time consuming and sample deficient. Only fast and low-cost assessment of grape smoke exposure can allow acre-by-acre inspection and support harvest decisions by the damage level.
We leverage artificial intelligence and proprietary sensors to make this assessment possible within minutes.