食品科学 ›› 2010, Vol. 31 ›› Issue (18): 283-287.doi: 10.7506/spkx1002-6630-201018066

• 分析检测 • 上一篇    下一篇

不同生长时期罗汉果高效液相指纹图谱研究

卢凤来,刘金磊,黄永林,李典鹏*   

  1. 广西壮族自治区- 中国科学院广西植物研究所
  • 收稿日期:2010-01-19 修回日期:2010-07-12 出版日期:2010-09-25 发布日期:2010-12-29
  • 通讯作者: 卢凤来 E-mail:lufenglai@gxib.cn
  • 基金资助:

    桂林市科技经费项目(20070304-2);广西植物研究所基本科研业务费资助项目(桂植业09010)

HPLC Fingerprints of Siraitia grosvenorii at Different Growth Stages

LU Feng-lai,LIU Jin-lei,HUANG Yong-lin,LI Dian-peng*   

  1. Guangxi Institute of Botany, Chinese Academy of Science, Guilin 541006, China
  • Received:2010-01-19 Revised:2010-07-12 Online:2010-09-25 Published:2010-12-29
  • Contact: LU Feng-lai E-mail:lufenglai@gxib.cn

摘要:

探索罗汉果药材的质量变化规律,确定罗汉果的关键生长期与采收期,指导罗汉果GAP 生产,同时为罗汉果药材建立一种更合理的质量控制方法。采用高效液相色谱法建立不同生长时期青皮果与长滩果的特征性化学成分指纹图谱,通过聚类分析以及对11 个指标性成分的鉴定及峰面积变化分析,研究罗汉果生长过程中的质量变化情况。结果表明不同栽培品种罗汉果在生长过程中具有相似的化学成分变化规律,不同生长时期化学成分特征不同。中药化学成分色谱指纹图谱技术是一种全方位多角度控制中药质量方法,可以更有效地指导中药材规范化种植。

关键词: 罗汉果, 色谱指纹图谱, 高效液相色谱法, 聚类分析

Abstract:

In order to explore quality change of Siraitia grosvenorii, confirm the crucial growth stage and the best harvesting season, guide the production of Siraitia grosvenorii according to GAP, and establish a method of quality control, high performance liquid chromatography (HPLC) was used to analyze the fingerprints of characteristic chemical components in Siraitia grosvenorii at different growth stages. Meanwhile, quality change of Siraitia grosvenorii at different growth stages was investigated through hierarchical cluster analysis and identification of characteristic peaks. Results showed that characteristic chemical components in Siraitia grosvenorii exhibited an obvious difference at various growth stages and different cultivated varieties. Therefore, fingerprinting of characteristic chemical components was an effective and all-directional quality control strategy for traditional Chinese herbal materials and promising application values for the guidance of herbal cultivation.

Key words: Siraitia grosvenorii, chromatogram fingerprint, HPLC, hierarchical cluster analysis

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