2016
DOI: 10.1186/s13065-016-0159-y
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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

Abstract: BackgroundModern 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 fingerprint 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 J… Show more

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Cited by 30 publications
(17 citation statements)
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“…HCA is an ideal technique for the crude classification of tea samples based on the contents of volatile components because it does not require previous information of test samples [ 38 ]. HCA of 21 samples was performed using a Ward’s method to visualize the differences and/or similarities among samples through Squared Euclidean distance.…”
Section: Resultsmentioning
confidence: 99%
“…HCA is an ideal technique for the crude classification of tea samples based on the contents of volatile components because it does not require previous information of test samples [ 38 ]. HCA of 21 samples was performed using a Ward’s method to visualize the differences and/or similarities among samples through Squared Euclidean distance.…”
Section: Resultsmentioning
confidence: 99%
“…In addition, GA 1 can be extracted from the roots of licorice in high yields (up to 24%) [ 22 ]. These findings have increased its scientific interest as a scaffold for the development of new derivatives for potential cancer treatment [ 23 , 24 , 25 , 26 , 27 ].…”
Section: Introductionmentioning
confidence: 99%
“…PCA can achieve an effect of dimensionality reduction and simplify complex data. After reducing dimensions, every sample obtained a score plot and based on the score plot, a classification was made . HCA is an intuitive classification method, dividing samples into categories according to the similar distance degree (in this study, the single linkage method was used) .…”
Section: Methodsmentioning
confidence: 99%