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Comparative Study of Volatile Components in Sun-Dried and Baked Black Teas in Yunnan Province

LÜ Shidong, WU Yuanshuang, WANG Chen, GAO Xuemei, LI Jiangbing, ZHANG Wenrui, MENG Qingxiong   

  1. 1. Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, China;
    2. Kunming Grain and Oil and Feed Product Quality Inspection Center, Kunming 650118, China;
    3. Yunnan Province Grain and Oil Products Quality Supervision and Inspection Station, Kunming 650118, China;
    4. Yunnan Kunming National Grain Quality Testing Station, Kunming 650118, China
  • Online:2016-07-25 Published:2016-07-26
  • Contact: MENG Qingxiong

Abstract:

The aroma components of sun-dried and baked black teas, three replicates each, extracted with headspace solidphase
microextraction (HS-SPME) were identified by gas chromatography-mass spectrometry (GC-MS). Their similarities
and differences were compared by using principal component analysis (PCA) and clustering analysis (CA). A total of
76 aroma components were identified in these two kinds of black tea, mainly including alcohols, ketones, and esters
compounds. Among them, alcohol compounds were the most abundant components in sun-dried black tea (57.43%) and
baked black tea (60.45%), mainly including linalool, linalool oxides, geraniol, nerolidol, etc. The aroma components of sundried
and baked black teas were very similar and difficult to distinguish from each other. However, PCA and CA showed
that sun-dried and baked black teas could be clearly distinguished according to their volatile characteristics, which illustrated
that drying methods have influences on aroma components of black tea. Therefore, HS-SPME-GC-MS in combination with
multivariate statistical methods could provide a feasible and rapid technique to differentiate between sun-dried and baked
black teas based on their volatile components and relative contents.

Key words: sun-dried black tea, baked black tea, aromat components, gas chromatography-mass spectrometry (GC-MS), multivariate statistical methods

CLC Number: