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CLC number: S571.1

On-line Access: 2012-12-07

Received: 2012-03-19

Revision Accepted: 2012-08-13

Crosschecked: 2012-10-31

Cited: 14

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE B 2012 Vol.13 No.12 P.972-980

http://doi.org/10.1631/jzus.B1200086


Volatile profile analysis and quality prediction of Longjing tea (Camellia sinensis) by HS-SPME/GC-MS


Author(s):  Jie Lin, Yi Dai, Ya-nan Guo, Hai-rong Xu, Xiao-chang Wang

Affiliation(s):  Institute of Tea Science, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   xcwang@zju.edu.cn

Key Words:  Partial least square (PLS) regression, Green tea, Headspace solid phase microextraction (HS-SPME), Volatile profile, Quality prediction


Jie Lin, Yi Dai, Ya-nan Guo, Hai-rong Xu, Xiao-chang Wang. Volatile profile analysis and quality prediction of Longjing tea (Camellia sinensis) by HS-SPME/GC-MS[J]. Journal of Zhejiang University Science B, 2012, 13(12): 972-980.

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author="Jie Lin, Yi Dai, Ya-nan Guo, Hai-rong Xu, Xiao-chang Wang",
journal="Journal of Zhejiang University Science B",
volume="13",
number="12",
pages="972-980",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B1200086"
}

%0 Journal Article
%T Volatile profile analysis and quality prediction of Longjing tea (Camellia sinensis) by HS-SPME/GC-MS
%A Jie Lin
%A Yi Dai
%A Ya-nan Guo
%A Hai-rong Xu
%A Xiao-chang Wang
%J Journal of Zhejiang University SCIENCE B
%V 13
%N 12
%P 972-980
%@ 1673-1581
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1200086

TY - JOUR
T1 - Volatile profile analysis and quality prediction of Longjing tea (Camellia sinensis) by HS-SPME/GC-MS
A1 - Jie Lin
A1 - Yi Dai
A1 - Ya-nan Guo
A1 - Hai-rong Xu
A1 - Xiao-chang Wang
J0 - Journal of Zhejiang University Science B
VL - 13
IS - 12
SP - 972
EP - 980
%@ 1673-1581
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B1200086


Abstract: 
This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyzed by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Pearson’s linear correlation analysis and partial least square (PLS) regression were applied to investigate the relationship between sensory aroma scores and the volatile compounds. Results showed that 60 volatile compounds could be commonly detected in this famous green tea. Terpenes and esters were two major groups characterized, representing 33.89% and 15.53% of the total peak area respectively. Ten compounds were determined to contribute significantly to the perceived aroma quality of Longjing tea, especially linalool (0.701), nonanal (0.738), (Z)-3-hexenyl hexanoate (−0.785), and β-ionone (−0.763). On the basis of these 10 compounds, a model (correlation coefficient of 89.4% and cross-validated correlation coefficient of 80.4%) was constructed to predict the aroma quality of Longjing tea. Summarily, this study has provided a novel option for quality prediction of green tea based on HS-SPME/GC-MS technique.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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