Fingerprinting black tea: when spectroscopy meets machine learning a novel workflow for geographical origin identification

Yicong Li, Natasha Logan, Brian Quinn, Yunhe Hong, Nicholas Birse, Hao Zhu, Simon Haughey, Christopher T. Elliott, Di Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.
Original languageEnglish
Article number138029
Number of pages12
JournalFood Chemistry
Volume438
Early online date25 Nov 2023
DOIs
Publication statusPublished - 16 Apr 2024

Keywords

  • Black tea
  • FTIR
  • Food fraud
  • Geographical indication
  • Machine learning
  • NIR

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