Evidence map›Paper›PMID 41708625›Full record

ArticleNPJ science of food2026

Geochemical fingerprinting and machine learning for authenticating sparkling wine origins.

Yihang Lu, Carola Doerr, Mathieu Sebilo

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yihang LuSorbonne Université, CNRS, INRAE, IRD, Institut d'Ecologie et des Sciences de l'Environnement de Paris (IEES), Paris, France. yihang.lu@sorbonne-universite.fr.
Carola Doerr *Sorbonne Université, CNRS, LIP6, Paris, France.
Mathieu Sebilo *Sorbonne Université, CNRS, INRAE, IRD, Institut d'Ecologie et des Sciences de l'Environnement de Paris (IEES), Paris, France. mathieu.sebilo@sorbonne-universite.fr.

Funding

This project is co-funded by the European Union's Horizon Europe research and innovation programme Cofund SOUND.AI under the Marie Sk lodowska-Curie Grant Agreement No 101081674 101081674
6 · The paper itself

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 (

Identifiers

PMID41708625
PMCPMC13031961

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.