Leveraging volatile sensing for rapid detection of smoke-derived signatures in grapes and wines

Abstract

Smoke-exposed grapes accumulate volatile organic compounds (VOCs) and their glycoconjugate precursors that can be released during fermentation and impart unpleasant, smoky, and ashy flavors to wines, complicating harvest decisions and potentially compromising wine quality. Current diagnostic methods rely on GC-MS/LC-MS analyses and small-scale fermentations that are time-consuming and costly. As smoke events continue to impact the California wine industry, there is a growing need for rapid, cost-effective methods to assess smoke impact risk in grapes and wine. This study explores the use of a novel, commercially available Signature Analysis Machine (SAM) that leverages differential sensing and data models to classify samples based on VOC signatures rapidly. Using wines across various cultivars from the 2020 and 2021 vintages, the SAM was able to differentiate smoke-impacted wines from non-impacted wines with classification driven by smoke exposure rather than variety or vintage. GC-MS and LC-MS chemical analyses were performed as validation for all samples to quantify smoke-derived volatile phenols and their glycoconjugates, respectively. Application of the SAM for the detection of smoke impact is currently being extended to grape samples collected from Napa Valley appellations affected by the Pickett Fire in 2025. Results suggest that rapid VOC fingerprinting may enable detection and classification of smoke-derived signatures. Continued integration of VOC signals with chemical and sensory data to construct a library of validated smoke exposure signatures may enable rapid screening of grape samples and risk assessment. Similar approaches are also being explored for the detection of Fresh Mushroom Aroma (FMA) in France, supporting the broader applicability of this strategy to vineyard-derived sensory risks.

References

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