Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Comparative analysis of volatiles difference of Yunnan sun-dried Pu-erh green tea from different tea mountains: Jingmai and Wuliang mountain by chemical fingerprint similarity combined with principal component analysis and cluster analysis

Comparative analysis of volatiles difference of Yunnan sun-dried Pu-erh green tea from different... Background: Modern instrumental analysis technology can provide various chemical data and information on tea samples. Unfortunately, it remains difficult to extract the useful information. We describe the use of chemical finger - print similarities, combined with principal component and cluster analyses, to distinguish and recognize Pu-erh green teas, which from two tea mountains, Wuliang and Jingmai, in the Pu-erh district of Yunnan province. The volatile components of all 20 Pu-erh green teas (10 Wuliang and 10 Jingmai teas) were extracted and identified by headspace solid-phase micro extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). Results: Sixty-three volatiles (including alcohols, hydrocarbons, ketones, and aldehydes) were identified in the 20 Pu- erh green teas, and differences in compound compositions between them were also observed. Through fingerprint similarity, combined with principal component and cluster analyses, the 20 Pu-erh green teas were differentiated successfully based on their volatile characteristics. Conclusions: This study demonstrates that the GC-MS combined with chemical fingerprint and unsupervised pattern recognition method is suitable for the investigation of the volatile profiling and evaluating the quality and authenticity of teas related to the different origins. Keywords: Pu-erh green tea, Gas chromatography-mass spectrometry, Chemical fingerprint similarity, Principal component analysis, Cluster analysis and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Chemistry Central Journal Springer Journals

Comparative analysis of volatiles difference of Yunnan sun-dried Pu-erh green tea from different tea mountains: Jingmai and Wuliang mountain by chemical fingerprint similarity combined with principal component analysis and cluster analysis

Loading next page...
 
/lp/springer-journals/comparative-analysis-of-volatiles-difference-of-yunnan-sun-dried-pu-vb400voG35

References (38)

Publisher
Springer Journals
Copyright
Copyright © 2016 by Wu et al.
Subject
Chemistry; Chemistry/Food Science, general
eISSN
1752-153X
DOI
10.1186/s13065-016-0159-y
pmid
26966460
Publisher site
See Article on Publisher Site

Abstract

Background: Modern instrumental analysis technology can provide various chemical data and information on tea samples. Unfortunately, it remains difficult to extract the useful information. We describe the use of chemical finger - print similarities, combined with principal component and cluster analyses, to distinguish and recognize Pu-erh green teas, which from two tea mountains, Wuliang and Jingmai, in the Pu-erh district of Yunnan province. The volatile components of all 20 Pu-erh green teas (10 Wuliang and 10 Jingmai teas) were extracted and identified by headspace solid-phase micro extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). Results: Sixty-three volatiles (including alcohols, hydrocarbons, ketones, and aldehydes) were identified in the 20 Pu- erh green teas, and differences in compound compositions between them were also observed. Through fingerprint similarity, combined with principal component and cluster analyses, the 20 Pu-erh green teas were differentiated successfully based on their volatile characteristics. Conclusions: This study demonstrates that the GC-MS combined with chemical fingerprint and unsupervised pattern recognition method is suitable for the investigation of the volatile profiling and evaluating the quality and authenticity of teas related to the different origins. Keywords: Pu-erh green tea, Gas chromatography-mass spectrometry, Chemical fingerprint similarity, Principal component analysis, Cluster analysis and

Journal

Chemistry Central JournalSpringer Journals

Published: Mar 10, 2016

There are no references for this article.