Citation: LI Qing-Yun, HUA Lei, HE Meng-Qi, LI Jia, JIANG JI-Chun, HOU Ke-Yong, TIAN Di, LI Hai-Yang. High-Pressure Photoionization/Chemical Ionization-Time-of-Flight Mass Spectrometry for Classification and Identification of Green Tea Aromas[J]. Chinese Journal of Analytical Chemistry, ;2019, 47(4): 541-549. doi: 10.19756/j.issn.0253-3820.181812 shu

High-Pressure Photoionization/Chemical Ionization-Time-of-Flight Mass Spectrometry for Classification and Identification of Green Tea Aromas

LI Qing-Yun ^1, ,
HUA Lei ² ,
HE Meng-Qi ³ ,
LI Jia ⁴ ,
JIANG JI-Chun ² ,
HOU Ke-Yong ² ,
TIAN Di ¹ ,
LI Hai-Yang ²

1.
College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130023, China;
2.
Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China;
3.
College of Physics, Dalian University and Technology, Dalian 116023, China;
4.
Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China

Received Date: 28 December 2018
Revised Date: 31 January 2019

Fund Project: This work was supported by the National Natural Science Foundation of China (Nos. 21675155, 21605142).

As the basic element of tea, aroma is one of the most important factors determining the quality of tea. In this paper, a novel and objective method for rapid discrimination and classification of green tea aromas was developed by coupling of dynamic headspace sampling high-pressure photoionization/chemical ionization-time-of-flight mass spectrometer (HPPI-TOFMS) and multivariate statistical analysis techniques, such as partial least squares discrimination analysis (PLS-DA) and layer cluster analysis (HCA). The classification efficiency was evaluated by analyzing the receiver operating characteristic (ROC) curve. The observations and the robustness were assessed using 200 permutation tests. The volatile fingerprint mass spectra of 31 green tea samples of 4 sub-types (tender chestnut-like, tender-like, chestnut-like and roasted chestnut-like aroma) collected from different production areas in Sichuan, Guizhou, Zhejiang, Anhui, etc, were obtained based on this method. In the HPPI/CI mass spectra of the aroma VOCs from different sub-types of green teas, the types and relative intensities of the characteristic mass peaks were obviously different. The results of multivariate statistical analysis showed that there were little differences among the aroma VOCs of the same sub-type, but significant differences were exhibited among different sub-types, which could achieve accurate classification and identification. The prediction ability of the model for unknown samples reaches 85.1%. This method provided a new idea and approach for the effective identification of green tea aromas, indicating that it had potential application value and broad development prospects in the fields of food quality and safety evaluation.
- High-pressure photoionization/chemical ionization,
- Time-of-flight mass spectrometry,
- Green tea aromas,
- Multivariate analysis,
- Volatiles fingerprinting
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