Abstract
The global wine market faces persistent threats from counterfeiting, particularly for high-value segments like sparkling wines. Traditional authentication methods relying on supply chain traceability and geographical indications are insufficient to curb rampant fraud, posing economic and health risks. This study pioneers a scalable geochemical fingerprinting framework, combining isotopic and elemental analyses with advanced machine learning, to authenticate sparkling wine origins. Using 75 French sparkling wine samples from Champagne and Burgundy, we achieved 100% classification accuracy with strontium isotopic ratios (87Sr/86Sr), which uniquely reflect geological characteristics of vineyards. To mitigate high analytical costs, the concentration of rubidium (Rb) was identified as a cost-effective alternative, reducing expenses by 75% while maintaining over 90% accuracy.
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Data availability
The dataset used in this study is not publicly available due to confidentiality agreements with the industrial partner. However, it can be obtained from the corresponding authors upon reasonable request for academic use.
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Acknowledgements
This study is co-funded by the European Union’s Horizon Europe research and innovation programme Cofund SOUND.AI under the Marie Skłodowska-Curie Grant Agreement No. 101081674. We thank Olivier Donard and Sylvain Berail (IPREM and AIA) andStanislas Milcent, Patrick Ors and Robin Cellier (Moët Hennessy) for providing the experimental dataset used in this research. The original doctoral project generating this data is documented in publicly available publications.
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Y.L.: conceptualization, data curation, methodology, formal analysis, writing—original draft. M.S.: resources, supervision, writing—review & editing. C.D.: methodology, supervision, writing—review & editing.
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Lu, Y., Doerr, C. & Sebilo, M. Geochemical fingerprinting and machine learning for authenticating sparkling wine origins. npj Sci Food (2026). https://doi.org/10.1038/s41538-025-00635-0
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DOI: https://doi.org/10.1038/s41538-025-00635-0
