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

doi:10.1016/j.foodchem.2023.138029

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

Food Chem. 2024 Apr 16:438:138029. doi: 10.1016/j.foodchem.2023.138029. Epub 2023 Nov 25.

Authors

Yicong Li¹, Natasha Logan¹, Brian Quinn¹, Yunhe Hong¹, Nicholas Birse¹, Hao Zhu¹, Simon Haughey¹, Christopher T Elliott², Di Wu³

Affiliations

¹ National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK.
² National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK; School of Food Science and Technology, Faculty of Science and Technology, Thammasat University (Rangsit Campus), Khlong Luang, Pathum Thani 12120, Thailand.
³ National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK. Electronic address: d.wu@qub.ac.uk.

PMID: 38006696
DOI: 10.1016/j.foodchem.2023.138029

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.

Keywords: Black tea; FTIR; Food fraud; Geographical indication; Machine learning; NIR.

MeSH terms

Camellia sinensis* / chemistry
Machine Learning
Spectroscopy, Fourier Transform Infrared / methods
Spectroscopy, Near-Infrared / methods
Tea* / chemistry
Workflow

Substances

Tea