Promoting LC-QToF based non-targeted fingerprinting and biomarker selection with machine learning for the discrimination of black tea geographical origin.
Yicong Li, Nicholas Birse, Yunhe Hong, Brian Quinn, Natasha Logan, Yanna Jiao, Christopher T Elliott, Di Wu
Author Information
Yicong Li: 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 BT9 5DL, Northern Ireland, United Kingdom.
Nicholas Birse: 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 BT9 5DL, Northern Ireland, United Kingdom.
Yunhe Hong: 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 BT9 5DL, Northern Ireland, United Kingdom.
Brian Quinn: 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 BT9 5DL, Northern Ireland, United Kingdom.
Natasha Logan: 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 BT9 5DL, Northern Ireland, United Kingdom.
Yanna Jiao: 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 BT9 5DL, Northern Ireland, United Kingdom; Hunan Provincial Key Laboratory of Food Safety Science and Technology: Technology Centre of Changsha Customs, 188 Xiangfu Middle Road, Changsha, Hunan 410000, China.
Christopher T Elliott: 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 BT9 5DL, Northern Ireland, United Kingdom; School of Food Science and Technology, Faculty of Science and Technology, Thammasat University (Rangsit Campus), Khlong Luang, Pathum Thani 12120, Thailand.
Di Wu: 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 BT9 5DL, Northern Ireland, United Kingdom. Electronic address: d.wu@qub.ac.uk.
Traceability and mislabelling of black tea for their geographical origin is known as a major fraud concern of the sector. Discrimination among various geographical indications (GIs) can be challenging due to the complexity of chemical fingerprints in multi-class metabolomics analysis. In this study, 302 black tea samples from 9 main cultivation GI regions were collected. A comprehensive non-targeted fingerprinting workflow was built on liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF), and a comparison between conventional chemometrics modelling and machine learning was performed. 229 and 145 metabolites were selected as biomarkers and the model robustness/performance were further validated through internal 7-fold cross-validation and external validation, showing 100 % accuracy for discriminating GI origin on both. This research provided a novel solution to enhance transparency and traceability in the black tea supply chain for lab scenarios. Furthermore, the proposed biomarker selection workflow revealed more insights for future machine learning-derived non-targeted metabolomics research.
