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Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.
PLoS One. 2018; 13(3):e0193393.Plos

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.

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Authors+Show Affiliations

Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China.

Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China.

Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China.

Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China.

Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China.

Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China.

MeSH

Cluster AnalysisDiscriminant AnalysisGas Chromatography-Mass SpectrometryInfrared RaysLeast-Squares AnalysisMultivariate AnalysisSolid Phase MicroextractionTeaVolatile Organic Compounds

Pub Type(s)

Journal Article

Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

29494626

Citation

Yang, Yan-Qin, et al. "Characterization of the Volatile Components in Green Tea By IRAE-HS-SPME/GC-MS Combined With Multivariate Analysis." PloS One, vol. 13, no. 3, 2018, pp. e0193393.

Yang YQ, Yin HX, Yuan HB, et al. Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis. PLoS One. 2018;13(3):e0193393.

Yang, Y. Q., Yin, H. X., Yuan, H. B., Jiang, Y. W., Dong, C. W., & Deng, Y. L. (2018). Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis. PloS One, 13(3), e0193393. https://doi.org/10.1371/journal.pone.0193393

Yang YQ, et al. Characterization of the Volatile Components in Green Tea By IRAE-HS-SPME/GC-MS Combined With Multivariate Analysis. PLoS One. 2018;13(3):e0193393. PubMed PMID: 29494626.

* Article titles in AMA citation format should be in sentence-case

TY - JOUR T1 - Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis. AU - Yang,Yan-Qin, AU - Yin,Hong-Xu, AU - Yuan,Hai-Bo, AU - Jiang,Yong-Wen, AU - Dong,Chun-Wang, AU - Deng,Yu-Liang, Y1 - 2018/03/01/ PY - 2017/08/01/received PY - 2018/02/10/accepted PY - 2018/3/2/entrez PY - 2018/3/2/pubmed PY - 2018/6/26/medline SP - e0193393 EP - e0193393 JF - PloS one JO - PLoS One VL - 13 IS - 3 N2 - 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. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/29494626/Characterization_of_the_volatile_components_in_green_tea_by_IRAE_HS_SPME/GC_MS_combined_with_multivariate_analysis_ DB - PRIME DP - Unbound Medicine ER -