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Updated: Sep 4, 2025
Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
Published on: May 20, 2013
A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate
Chuan-Yi Peng1, Yin-Feng Ren1, Zhi-Hao Ye1
1State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, People's Republic of China; Key Laboratory of Food Nutrition and Safety, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei 230036, Anhui, People's Republic of China.
Abstract:
Geographic-label is a remarkable feature for Chinese tea products. In this study, the UHPLC-Q/TOF-MS-based metabolomics approach coupled with chemometrics was used to determine the five narrow-geographic origins of Keemun black tea. Thirty-nine differentiated compounds (VIP > 1) were identified, of which eight were quantified. Chemometric analysis revealed that the linear discriminant analysis (LDA) classification accuracy model is 91.7%, with 84.7% cross-validation accuracy. Three machine learning algorithms, namely feedforward neural network (FNN), random forest (RF) and support vector machine (SVM), were introduced to improve the recognition of narrow-geographic origins, the performances of the model were evaluated by confusion matrix, receiver operating characteristic curve (ROC) and area under the curve (AUC). The recognition of RF, SVM and FNN for Keemun black tea from five narrow-geographic origins were 87.5%, 94.44%, and 100%, respectively. Importantly, FNN exhibited an excellent classification effect with 100% accuracy. The results indicate that metabolomics fingerprints coupled with chemometrics can be used to authenticate the narrow-geographic origins of Keemun black teas.
