A comprehensive review on wine authentication and traceability: Current issues, solutions and future perspectives
Clinical Snapshot
PICO Framework
| P — Population | Wine products and wine industry stakeholders |
| I — Intervention | Modern analytical techniques (spectroscopy, chromatography, mass spectrometry, intelligent sensing, machine learning) |
| C — Comparator | Traditional authentication methods or no authentication |
| O — Outcomes | Wine authenticity verification, geographical origin traceability, fraud detection accuracy |
Bottom Line
This narrative review examines analytical techniques for wine authentication and traceability but lacks the methodological rigor expected of systematic reviews. While it addresses an important commercial issue affecting wine fraud detection, the absence of systematic search methodology, quality assessment, and quantitative synthesis limits its evidence value. The review identifies promising technologies including spectroscopy, chromatography, and machine learning approaches, but provides insufficient detail to guide evidence-based implementation. Future systematic reviews with proper methodology would better serve stakeholders seeking robust evidence for authentication technology adoption. This work functions more as a general overview than a critical evidence synthesis.
Key Findings
P Value: Not applicable - no statistical analysis
Effect Size: Not quantified - narrative summary only
Primary Outcome: Wine authentication and traceability methods effectiveness
Nnt Or Sensitivity: Detection accuracy varies by technique but not systematically reported
Confidence Interval: Not provided
Clinical Application
Not applicable to clinical practice Not relevant to Australian healthcare - this is food industry focused Not applicable - this is food science research, not clinical medicine
Abstract
Wine, as a commercially significant and globally consumed beverage, faces persistent challenges regarding authenticity verification and origin traceability, which threaten both consumer interests and market fairness. This paper comprehensively summarizes prevalent types of wine fraud issues and reviews modern analytical techniques including spectroscopy, chromatography, mass spectrometry, and emerging intelligent sensing techniques alongside advanced machine learning approaches for enhancing authentication and traceability of wine. The study highlights recent applications of these methods in verifying wine authenticity and geographical origins, emphasizing their evolving integration. Despite considerable progress, future development should focus on rapid detection, integrating deep learning with traditional analytical techniques, building wine fingerprint database, and leveraging blockchain for supply chain transparency. These measures are crucial to ensure the authenticity and geographical origins of wine, thereby safeguarding the interests of all stakeholders, including consumers and producers.
References
- 1.Qiang, H.-Q., Gu, H.-W., Yin, X.-L., Meng, Z., Long, W., Fu, H., & She, Y. (2026). A comprehensive review on wine authentication and traceability: Current issues, solutions and future perspectives. Food Chemistry, 148456. https://doi.org/10.1016/j.foodchem.2026.148456
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