Analysis of Seven Terpenoids by HS-SPME Coupled with GC-MS for the Identification and Classification of Different Teas
Yangzhou Xie, Yi Yang, Yu Tian, Zhimin Liu, Zhigang Xu, Wei Jiang, Zhihua Liu, Xiaoxi SiBackground:
Terpenoids are essential aroma substances in teas, and their concentration brings various characteristics to different teas. Therefore, developing a simple and stable method is necessary for distinguishing tea categories.
Objective:
In previous studies, more attention was paid to non-chiral isomers of terpenes due to the challenges of separating chiral isomers. So, this paper aims to present a method for effectively separating seven terpenoid substances, including chiral isomers and non-chiral isomers, to facilitate the classification and identification of teas.
objective:
In previous studies, more attention was paid to non-chiral isomers of terpeniods due to the challenges with separating chiral isomers. So, this paper aims to present a method for effectively separating seven terpenoid substances, including chiral isomers and non-chiral isomers, to facilitate the classification and identification of teas.
Methods:
A method utilizing headspace solid-phase microextraction coupled with gas chromatography- mass spectrometry was used to isolate and analyze 7 terpenoid compounds. After optimized conditions, the BGB-176 chiral column and the PDMS/DVB fiber were selected for subsequent analysis.
Results:
This method has a good linear range of 0.1-200 mg/L, and its linear correlation coefficients are between 0.9974 and 0.9994, and the limit of detection and the limit of quantification is 0.02–0.03 and 0.06–0.09 mg/L, respectively. Only five terpenoid substances were detected in a total of 15 tea samples. Furthermore, In the detection of carvon and α-ionone optical isomers, the S isomer was mainly detected.
Conclusions:
An effective approach was developed to separate and analyze 7 terpenoid compounds in natural and synthetic teas. Meanwhile, 15 tea samples can be identified and classified using principal component analysis.