萜类
鉴定(生物学)
气相色谱-质谱法
色谱法
化学
传统医学
质谱法
立体化学
医学
生物
植物
作者
Yangzhou Xie,Yi Yang,Yu Tian,Zhimin Liu,Zhigang Xu,Wei Xiang Jiang,Zhihua Liu,Xiaoxi Si
出处
期刊:Current Analytical Chemistry
[Bentham Science]
日期:2024-05-07
卷期号:20 (9): 629-636
标识
DOI:10.2174/0115734110301044240426170020
摘要
Background: 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.
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