Acta Chimica Sinica ›› 2012, Vol. 70 ›› Issue (01): 78-82.DOI: 10.6023/A1105164 Previous Articles     Next Articles

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莪术药材的闪蒸气相色谱测定及其模式识别分析研究

刘会君a, 陈爽b, 王鹏a, 陆璐a, 潘再法a, 王丽丽a   

  1. a. 浙江工业大学化学工程与材料学院, 杭州 310014;
    b. 浙江省食品药品检验所, 杭州 310004
  • 投稿日期:2011-06-16 修回日期:2011-09-09 发布日期:2012-02-25
  • 通讯作者: 王丽丽 E-mail:lili_wang@zjut.edu.cn
  • 基金资助:

    浙江省重点科技创新团队(No.2011R09002-11)和浙江省重中之重开放基金(No.200908)资助项目.

Flash Evaporation-Gas Chromatography Determination with Pattern Recognition Analysis for Curcuma zedoaria Rosc.

Liu Huijuna, Chen Shuangb, Wang Penga, Lu Lua, Pan Zaifaa, Wang Lilia   

  1. a. College of Chemical Engineering and Materials Science, Zhejiang University of Technology, Hangzhou 310014;
    b. Zhejiang Institute for Food and Drug Control, Hangzhou 310004
  • Received:2011-06-16 Revised:2011-09-09 Published:2012-02-25
  • Supported by:

    Project was supported by the Key Science and Technology Innovation Team of Zhejiang Province(No.2011R09002-11)and the Opening Foundation of Zhejiang Provincial Top Key Discipline(No.200908).

A method of flash evaporation followed by gas chromatography(FE-GC)was developed for de-termination of volatile components of Curcuma zedoaria Rosc.The Curcuma zedoaria Rosc.samples from 3 different growing places were analyzed on the basis of flash evaporation-gas chromatography(FE-GC).An amount 0.4 mg of sample powder was evaporated in a micro-furnace pyrolyzer at 200℃,and the products formed were introduced into a gas chromatograph equipped with a flame ionization detector or mass spec-trometer.Based on the relative peak areas of 35 peaks in the chromatogram obtained by FE-GC,9 samples of Curcuma zedoaria Rosc.from 3 different origins were clearly classified into 3 categories by two pattern recognition analysis,i.e.principle component analysis and hierarchical cluster analysis.The results proved that it is a simple,rapid and accurate method suitable for quality control of the traditional Chinese medi-cines.

Key words: Curcuma zedoaria Rosc., flash evaporation, gas chromatography, principal component analysis, hierarchical cluster analysis