Quality Prediction of Japanese Green Tea Using Pyrolyzer Coupled GC/MS Based Metabolic Fingerprinting

A couple between pyrolyzer and gas chromatography/mass spectrometry (GC/MS) has allowed a fast, simple, and low-cost approach to evaluate a quality of Japanese green tea without any sample preparation or derivatization techniques. Using our method, errors from sample preparation could be avoided sin...

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Bibliographic Details
Published in:Journal of agricultural and food chemistry 2008-02, Vol.56 (3), p.744-750
Main Authors: Pongsuwan, Wipawee, Bamba, Takeshi, Yonetani, Tsutomu, Kobayashi, Akio, Fukusaki, Eiichiro
Format: Article
Language:English
Subjects:
Analytical Methods
Biological and medical sciences
Camellia sinensis
Coffee, tea and other stimulative beverage industries
Fish and seafood industries
food analysis
food composition
Food industries
food quality
Fundamental and applied biological sciences. Psychology
gas chromatography
Gas Chromatography-Mass Spectrometry
green tea
Hot Temperature
Japan
Least-Squares Analysis
mass spectrometry
Metabolic fingerprinting
metabolome
metabolomics
plant genetics
prediction
prediction model
PY-GC/MS
pyrolysis
pyrolyzer coupled gas chromatography mass spectrometry
Quality Control
quality of green tea
Tea - chemistry
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Summary:A couple between pyrolyzer and gas chromatography/mass spectrometry (GC/MS) has allowed a fast, simple, and low-cost approach to evaluate a quality of Japanese green tea without any sample preparation or derivatization techniques. Using our method, errors from sample preparation could be avoided since raw samples were directly extracted through the extreme heat of the pyrolyzer. In addition, undesired reactions from expensive derivatizing agents, which are commonly needed to treat the samples before analyzing with GC/MS, could be omitted. In order to illustrate the efficiency of this technique, a set of green tea samples from the Tea contest in 2005 in the Kansai area were used. Projection to latent structure by means of partial least squares (PLS) along with orthogonal signal correction (OSC) was selected to explain the relation between green tea’s metabolite profiling and its quality. The quality of the model was validated by testing and comparing the predictive ability to the respective model.
ISSN:0021-8561
1520-5118
DOI:10.1021/jf072791v