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Analysis of chemical components in green tea in relation with perceived quality, a case study with Longjing teas

Authors

  • Kunbo Wang,

    1.  Tea Research Institute of Chinese Academy of Agricultural Sciences and the Key Laboratory of Tea Chemical Engineering of the Ministry of Agriculture, Hangzhou, Zhejiang 310008, China
    2.  Key Laboratory of Tea Science of Ministry of Education and Hunan Province Key Laboratory of Crop Germplasm Innovation and Utilisation, Hunan Agricultural University, Changsha, Hunan 410128, China
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  • Jianyun Ruan

    Corresponding author
    1.  Tea Research Institute of Chinese Academy of Agricultural Sciences and the Key Laboratory of Tea Chemical Engineering of the Ministry of Agriculture, Hangzhou, Zhejiang 310008, China
      *Correspondent: Fax: +86 571 86650056; e-mail: jruan@mail.tricaas.com
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*Correspondent: Fax: +86 571 86650056; e-mail: jruan@mail.tricaas.com

Summary

Properties of leaf and infusion colours, chemical components and volatile flavour compounds of Longjing teas and their correlation with perceived quality score given by tea-tasting panel were analysed. The scores for appearance, infused leaves and for the Total Quality Score (TQS) negatively correlated with concentrations of chlorophyll-a (Chl-a), Chl-b and total chlorophyll (r = −0.723 to −0.846, P < 0.01). The perceived taste score positively correlated with the concentration of total free amino acid (r = 0.527, P < 0.05) and theanine (r = 0.511, P < 0.05), whereas the TQS positively correlated with total free amino acid (r = 0.533). The volatile composition and their quantities varied widely among Longjing tea samples. (E)-geraniol, linalool, hexanal, pentanal, heptanal, nonanal and jasmone were prevailing volatile compounds detected in all samples. Principal component analysis (PCA) screened twelve principal components with the first three explaining 18.82%, 15.31% and 11.83% of the total variance respectively. The concentrations of total free amino acids, pentanal and epigallocatechin gallate were with the highest component score coefficients for the first three principal components respectively. Regression analysis upon the twelve principal components formulated a prediction model on the TQS with 77.0% probability.

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