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Analysis of chemical components in oolong tea in relation to perceived quality
Author(s) -
Wang Kunbo,
Liu Fang,
Liu Zhonghua,
Huang Jianan,
Xu Zhongxi,
Li Yinhua,
Chen Jinhua,
Gong Yushun,
Yang Xinghe
Publication year - 2010
Publication title -
international journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.831
H-Index - 96
eISSN - 1365-2621
pISSN - 0950-5423
DOI - 10.1111/j.1365-2621.2010.02224.x
Subject(s) - linalool , chemistry , nerolidol , flavour , food science , aroma , geraniol , theanine , hexanal , principal component analysis , essential oil , green tea , mathematics , statistics
Summary Properties of leaf and infusion colours, chemical components and volatile flavour compounds of oolong teas and their correlation with perceived quality score given by tea‐tasting panel were analysed. The scores for appearance and infused leaf correlated strongly with concentrations of chlorophyll a (chl a ), chlorophyll (chl b ) and chlorophyll (chl) ( r = 0.355–0.433, P < 0.05) and the total quality score (TQS) positively correlated with concentrations of chl a , chl b and chl ( r = 0.517–0.533, P < 0.01). The perceived taste score and TQS positively correlated with the concentration of total free amino acid ( r = 0.514, 0.694, P < 0.01) and theanine ( r = 0.500, 0.684, P < 0.01). The volatile composition and their quantities varied widely among oolong tea samples. Nerolidol, indole, benzeneacetaldehyde, linalool, linalool oxide I, hexanal, benzyl nitrile, geraniol and 1‐penten‐3‐ol were prevailing volatile compounds detected in most of oolong tea samples. Principal component analysis screened ten principal components with the first three (glutamic acid, total catechins and benzeneacetaldehyde) explaining 27.86%, 20.00% and 14.46% of the total variance, respectively. Regression analysis upon the ten principal components formulated a prediction model on the total quality score with 78.5% probability. The result showed that oolong teas could be partially classified by cluster analysis based on principal components.