Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

Yan-Qin Yang, Hong-Xu Yin, Hai-Bo Yuan, Yong-Wen Jiang, Chun-Wang Dong and Yu-Liang Deng

PLOS ONE, 2018, vol. 13, issue 3, 1-19

Abstract: In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0193393

DOI: 10.1371/journal.pone.0193393

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