Promoting LC-QToF based non-targeted fingerprinting and biomarker selection with machine learning for the discrimination of black tea geographical origin
Y.F. Li, Nicholas Birse, Yunhe Hong, Brian Quinn, Natasha Logan, Yanna Jiao, Christopher T. Elliott, Di Wu
Abstract
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. • Black tea GI discrimination achieved using non-targeted LC-QToF fingerprinting techniques. • The 302 samples from 9 GIs achieved 100 % discrimination by machine learning algorithms. • A novel workflow for biomarker selection in non-targeted metabolomics was proposed. • The 229 and 145 biomarkers were found for identifying African and Asian GI regions.