Abstract:

Principal component analysis (PCA) and cluster analysis (CA) were used to determine, discriminate and classify
the volatile components of tea flowers from different cultivars including Sichuan small-leaf cultivars (population), Fuxuan No.
9, Fudingdabai, Fu’andabai, Jinguanyin and Meizhan at two stages of bloom, namely initial (stage Ⅰ) and full petal (stage Ⅱ)
expansion. Headspace solid phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GCMS)
was used to analyze the volatile components of 12 samples of tea flower based on their retention indices. The results
showed that 56 volatile components in tea flower were obtained, and PCA analysis suggested that 6 principal components
could reflect most of the information on the samples with a total cumulative variance contribution rate of 85.55%. PCA and
CA divided 12 tea flower samples into 2 clusters. One of the clusters was formed by 4 tea flower samples collected from the
two stages of Meizhan, stage Ⅱ of Sichuan small-leaf cultivars and stage Ⅱ of Jinguanyin. The other cluster was formed
by the remaining 8 tea flower samples. The same clusters were alike in flavor, and this could be used as the basis for mixed
collection and further processing of tea flowers.

Key words: tea flower, headspace solid phase microextraction-gas chromatography-mass spectrometry, retention indices, principal component analysis, cluster analysis