Related Experiment Video
Updated: Jun 13, 2025

Author Spotlight: Exploring Tea Aroma Using Solvent-Assisted Flavor Evaporation Technique
Published on: May 26, 2023
1H NMR Spectroscopy Combined with Machine-Learning Algorithm for Origin Recognition of Chinese Famous Green Tea
Zhiwei Hou1, Yugu Jin1, Zhe Gu1
1College of Tea Science and Tea Culture, Zhejiang A & F University, 666 Wusu Street, Hangzhou 311300, China.
Abstract:
Premium green tea is a high-value agricultural product significantly influenced by its geographical origin, making it susceptible to food fraud. This study utilized nuclear magnetic resonance (NMR) spectroscopy to perform chemical fingerprint analysis on 78 Longjing tea (LJT) samples from both protected designation of origin (PDO) regions (Zhejiang) and non-PDO regions (Sichuan, Guangxi, and Guizhou) in China. Unsupervised algorithms and heatmaps were employed for the visual analysis of the data from PDO and non-PDO teas while exploring the feasibility of linear and nonlinear machine-learning algorithms in discriminating the origin of LJT. The findings revealed that the nonlinear model random forest (92.2%), exhibited superior performance compared to the linear model linear discriminant analysis (85.6%). The random forest model identified 15 key marker metabolites for the geographical origin of LJT, such as kaempferol glycoside, glutamine, and ECG. The results support the conclusion that the integration of NMR with machine-learning classification serves as an effective tool for the quality assessment and origin identification of LJT.
More Related Videos
Related Concept Videos
IR Frequency Region: Fingerprint Region
¹H NMR Signal Integration: Overview
NMR Spectroscopy of Aromatic Compounds
¹H NMR: Interpreting Distorted and Overlapping Signals
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...

