Differentiating Westlake Longjing tea from the first- and second-grade producing regions using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics in combination with chemometrics
文献类型： 外文期刊
作者： Zhang, Qian 1 ; Zhang, Yu-Ying 1 ; Liu, Zhi 2 ; Zhang, Yue-Ming 1 ; Lu, Ning 1 ; Hai, Guo-Qing 1 ; Shao, Sheng-Zhi 2 ; Zh 1 ;
作者机构： 1.Ningxia Med Univ, Coll Pharm, Yinchuan 750004, Ningxia, Peoples R China
2.Zhejiang Acad Agr Sci, Inst Qual & Stand Agr Prod, Hangzhou, Peoples R China
3.Zhengzhou Tobacco Res Inst CNTC, Zhengzhou, Peoples R China
4.South Cent Univ Nationalities, Sch Pharmaceut Sci, Wuhan, Peoples R China
5.Zhejiang Univ Technol, Hangzhou, Peoples R China
6.Ningxia Med Univ, Ningxia Engn & Technol Res Ctr Modernizat Hui Med, Yinchuan, Ningxia, Peoples R China
关键词： chemometrics; geographical origin discrimination; untargeted metabolomics; Westlake Longjing tea
期刊名称：JOURNAL OF SEPARATION SCIENCE （ 影响因子：3.645； 五年影响因子：2.943 ）
ISSN： 1615-9306
年卷期：
页码：
收录情况： SCI
摘要： There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first- and second-grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high-resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two-way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.
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