ArticleNPJ science of food2026
Geochemical fingerprinting and machine learning for authenticating sparkling wine origins.
Article in NPJ science of food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Stable isotopes from Earth science to bedside medicine: beyond tracers and toward mechanistic biomarkers in medical oncology.Medical oncology (Northwood, London, England) · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
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 (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.