The classification, detection and ‘SMART’ control of the nine sins of tea fraud

Yicong Li, Christopher T. Elliott, Awanwee Petchkongkaew, Di Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background
Food fraud exists in all types of food and beverages, and everywhere in the world. Tea is the world’s most widely consumed beverage. Its global supply chain is complex and lacks transparency, making it extremely vulnerable to fraud and the vulnerability has not been fully understood. Therefore, creating a gap in control measurements for tea fraud prevention and mitigation.

Scope and approach
In this review, we examined reported tea fraud incidents from the European Union's Rapid Alert System for Food and Feed (RASFF) and the FoodAkai Food Fraud Database, spanning from June 2011 to July 2023. Additionally, the different forms of tea fraud perpetrated throughout supply chain, available analytical techniques to detect tea fraud, and control measures in place for tea fraud prevention and mitigation were summarized in this review.

Key findings and conclusion
A total of 9 different forms of tea fraud have been identified and categorised. Unapproved enhancement was the most prevalent type of tea fraud issue found, accounting for 43% of all cases. Mass spectrometry, vibrational spectroscopy, multi-elemental and stable isotype techniques, electronic sensors, nuclear magnetic resonance spectroscopy, and molecular biology techniques have been employed in tea fraud detection. At present, there is no single analytical tool capable of providing comprehensive decision-making regarding tea authenticity issues. It is necessary to use comprehensive technical control measures that involve multidisciplinary targeted determination and untargeted fingerprinting techniques, combined with AI supported multiple management control measures, to prevent and mitigate tea fraud issues.
Original languageEnglish
Article number 104565
JournalTrends in Food Science and Technology
Early online date27 May 2024
DOIs
Publication statusEarly online date - 27 May 2024

Fingerprint

Dive into the research topics of 'The classification, detection and ‘SMART’ control of the nine sins of tea fraud'. Together they form a unique fingerprint.

Cite this