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A comprehensive review on wine authentication and traceability: Current issues, solutions and future perspectives.

Hai-Qing Qiang1, Hui-Wen Gu1, Xiao-Li Yin1

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Ensuring wine authenticity and origin traceability is vital for consumers and markets. This study reviews analytical techniques and machine learning to combat wine fraud, proposing future advancements for supply chain transparency.

Keywords:
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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Agricultural Economics

Background:

  • Global wine market faces significant challenges with fraud impacting consumer trust and market integrity.
  • Authenticity and origin traceability are critical for maintaining fair trade and consumer confidence in wine products.

Purpose of the Study:

  • To comprehensively review prevalent wine fraud issues.
  • To assess modern analytical and machine learning techniques for wine authentication and traceability.
  • To identify future research directions for enhancing wine integrity.

Main Methods:

  • Review of scientific literature on analytical techniques (spectroscopy, chromatography, mass spectrometry, intelligent sensing).
  • Analysis of machine learning applications in wine authentication and geographical origin verification.
  • Synthesis of current research on database development and blockchain technology for supply chain transparency.

Main Results:

  • Various analytical and machine learning methods show promise in detecting wine fraud and verifying origin.
  • Integration of traditional analytical techniques with advanced machine learning is advancing wine authentication.
  • Emerging intelligent sensing techniques offer new avenues for rapid detection.

Conclusions:

  • Continued development in rapid detection methods is essential.
  • Integrating deep learning with traditional analytical techniques and building comprehensive wine fingerprint databases are key.
  • Leveraging blockchain technology can significantly enhance supply chain transparency and combat wine fraud.