FOOD SCIENCE ›› 2021, Vol. 42 ›› Issue (20): 173-179.doi: 10.7506/spkx1002-6630-20201006-020

• Component Analysis • Previous Articles     Next Articles

Analysis of Volatile Components of Toona sinensis from Different Growing Areas in Henan Province by Gas Chromatography-Mass Spectrometry Fingerprinting and Chemical Pattern Recognition

ZHAO Lili, CHENG Jingjing, WANG Zhaogai, SHI Guanying, ZHANG Le, WANG Xiaomin, JIANG Pengfei, WANG Xuzeng   

  1. (Agricultural Products Processing Center, Henan Academy of Agricultural Sciences, Zhengzhou 450000, China)
  • Online:2021-10-25 Published:2021-11-12

Abstract: In this study, headspace solid phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS) and the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (TCM) were used to establish a fingerprint for volatile components in the young leaves and buds of Toona sinensis from different growing areas in Henan Province. Meanwhile, a comprehensive evaluation on volatile components of T. sinensis was conducted through similarity evaluation and chemical pattern recognition (cluster analysis and principal component analysis). The results showed that the established standard fingerprint was consistent with the results of cluster analysis and principal component analysis, and all of them could clearly distinguish T. sinensis from different growing areas. The major components causing the flavor difference among different batches of T. sinensis were identified to be 2-hexenal, 2,4-dimethylthiophene, farnesol acetate and 2-methyl-3-methylene-cyclopentanecarboxaldehyde. Meanwhile, the comprehensive score for volatile components of T. sinensis planted on mountains in Xinyang was the highest. The results from this study may provide a theoretical basis and technical support for rapid geographical origin tracing and quality control of T. sinensis.

Key words: Toona sinensis; volatile components; gas chromatography-mass spectrometry; fingerprint; cluster analysis; principal component analysis

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