Simultaneous determination of eleven volatile components in Cinnamomi Oleum by GC-MS.
10.19540/j.cnki.cjcmm.20221208.103
- Author:
Yang ZHOU
1
;
Ting YAN
1
;
Lin ZHENG
2
;
Ming-Yan CHI
1
;
Zi-Peng GONG
2
;
Yue-Ting LI
2
;
Jie PAN
3
;
Yong HUANG
1
;
Qing-Bo YANG
4
Author Information
1. State Key Laboratory of Functions and Applications of Medicinal Plants,Guizhou Provincial Key Laboratory of Pharmaceutics,Guizhou Medical University Guiyang 550004,China School of Pharmaceutical Sciences,Guizhou Medical University Guiyang 550004,China.
2. State Key Laboratory of Functions and Applications of Medicinal Plants,Guizhou Provincial Key Laboratory of Pharmaceutics,Guizhou Medical University Guiyang 550004,China.
3. Engineering Research Center for Development and Application of Ethnic Medicine and TCM,Guizhou Medical University Guiyang 550004,China.
4. Guizhou Yibai Pharmaceutical Co.,Ltd. Guiyang 550008,China.
- Publication Type:Journal Article
- Keywords:
Cinnamomi Oleum;
GC-MS;
content determination;
hierarchical clustering analysis;
orthogonal partial least squares-discriminant analysis;
principal component analysis
- MeSH:
Gas Chromatography-Mass Spectrometry;
Plant Oils;
Oils, Volatile;
Drugs, Chinese Herbal/analysis*;
Cluster Analysis
- From:
China Journal of Chinese Materia Medica
2023;48(6):1568-1577
- CountryChina
- Language:Chinese
-
Abstract:
A gas chromatography-triple quadrupole mass spectrometry(GC-MS) method was established for the simultaneous determination of eleven volatile components in Cinnamomi Oleum and the chemical pattern recognition was utilized to evaluate the quality of essential oil obtained from Cinnamomi Fructus medicinal materials in various habitats. The Cinnamomi Fructus medicinal materials were treated by water distillation, analyzed using GC-MS, and detected by selective ion monitoring(SIM), and the internal standards were used for quantification. The content results of Cinnamomi Oleum from various batches were analyzed by hierarchical clustering analysis(HCA), principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) for the statistic analysis. Eleven components showed good linear relationships within their respective concentration ranges(R~2>0.999 7), with average recoveries of 92.41%-102.1% and RSD of 1.2%-3.2%(n=6). The samples were classified into three categories by HCA and PCA, and 2-nonanone was screened as a marker of variability between batches in combination with OPLS-DA. This method is specific, sensitive, simple, and accurate, and the screened components can be utilized as a basis for the quality control of Cinnamomi Oleum.
