Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractChanges of the free and bound volatile compounds in Rubus corchorifolius L. f. fruit during ripening    Next AbstractLC50 Determination of tert-Butyl Acetate using a Nose Only Inhalation Exposure in Rats »

PLoS One


Title:Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis
Author(s):Yang YQ; Yin HX; Yuan HB; Jiang YW; Dong CW; Deng YL;
Address:"Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, China"
Journal Title:PLoS One
Year:2018
Volume:20180301
Issue:3
Page Number:e0193393 -
DOI: 10.1371/journal.pone.0193393
ISSN/ISBN:1932-6203 (Electronic) 1932-6203 (Linking)
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"
Keywords:Cluster Analysis Discriminant Analysis *Gas Chromatography-Mass Spectrometry Infrared Rays Least-Squares Analysis Multivariate Analysis Solid Phase Microextraction Tea/*chemistry/metabolism Volatile Organic Compounds/*analysis/isolation & purification;
Notes:"MedlineYang, Yan-Qin Yin, Hong-Xu Yuan, Hai-Bo Jiang, Yong-Wen Dong, Chun-Wang Deng, Yu-Liang eng Research Support, Non-U.S. Gov't 2018/03/02 PLoS One. 2018 Mar 1; 13(3):e0193393. doi: 10.1371/journal.pone.0193393. eCollection 2018"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 19-12-2024