CHEMICAL CHARACTERIZATION AND CLASSIFICATION OF GEOGRAPHICAL ORIGIN OF VIETNAMESE GREEEN TEAS BASED ON 1H- NMR DATA COMBINED WITH MACHINE LEARNING
Abstract
Nuclear magnetic resonance spectroscopy (NMR) is widely used for analyzing biological origin samples such as coffee, honey, fruit juice, etc. In this study, the chemical composition of 34 samples of Vietnamese green teas were identified by 1H-NMR spectroscopy. The Vietnamese green tea samples collected in three provinces - Bac Kan, Thai Nguyen and Lao Cai were classified according to the age of tea leaves and tea trees, including ancient green tea and regular green tea as well as their geographical origin. The chemical compositions such as catechins, caffeine and some amino acids were identified in 1H-NMR spectra for both ancient tea and young green tea. Based on spectral pattern, the classification of tea samples was performed by partial least squares - discriminant analysis (PLS-DA) and sparse partial least squares - discriminant analysis (sPLS-DA) models using Metabo Analyst 5.0 software. The discriminate results showed that the age and biological origin of green teas were classified accurately by PLS-DA and sPLS-DA, reaching 82.6% and 81.2%, respectively. Furthermore, the classification results revealed a significant difference of Lao Cai and Bac Kan green tea, while Thai Nguyen green teas exhibited characteristics of both regions. The supervised learning-based classification models were applied to build database, classify, and identify green tea patterns based on 1H-NMR spectroscopic data.
For citation:
Pham Quang Trung, Hoang Bich Ngoc, Nguyen Van Thuc, Tran Thi Hue, Pham Gia Bach, Ta Thi Thao Chemical characterization and classification of geographical origin of vietnamese greeen teas based on 1H- NMR data combined with machine learning. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2023. V. 66. N 12. P. 56-64. DOI: 10.6060/ivkkt.20236612.6874.
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