Quality assessment of Cortex cinnamomi by HPLC chemical fingerprint, principle component analysis and cluster analysis

Abstract

HPLC fingerprint analysis, principle component analysis (PCA), and cluster analysis were introduced for quality assessment of Cortex cinnamomi (CC). The fingerprint of CC was developed and validated by analyzing 30 samples of CC from different species and geographic locations. Seventeen chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression of the HPLC fingerprints. The correlation coefficients of similarity in chromatograms were higher than 0.95 for the same species while much lower than 0.6 for different species. Besides, two principal components (PCs) have been extracted by PCA. PC1 separated Cinnamomum cassia from other species, capturing 56.75% of variance while PC2 contributed for their further separation, capturing 19.08% variance. The scores of the samples showed that the samples could be clustered reasonably into different groups corresponding to different species and different regions. The scores and loading plots together revealed different chemical properties of each group clearly. The cluster analysis confirmed the results of PCA analysis. Therefore, HPLC fingerprint in combination with chemometric techniques provide a very flexible and reliable method for quality assessment of traditional Chinese medicines.

Study of the Relationship between Genetics and Geography in Determining the Quality of Astragali Radix

“…20) PCA has been applied to quality assessment of plant extracts and natural products upon the data of HPLC chromatography. [21][22][23][24] PCA was performed. PCA is based on the derivation of linear combinations of the measured descriptors to produce new variables calls principal components (PCs) that are uncorrelated.…”

Comparison of ICP OES and LIBS Analysis of Medicinal Herbs Rich in Flavonoids from Eastern Europe

“…22 In order to interpret the huge databases of results obtained for determining elements in medicinal herbs or in other materials of natural origin, such as tea samples or honeys, multivariate statistical methods are very often required. [23][24][25][26][27] Based on the experimental results obtained by high performance liquid chromatography (HPLC) technique with various detection systems, it was found that after the application of principal component analysis (PCA) and cluster analysis (CA), samples of medicinal plants representing three botanical species, Equisetum, Polygonum and Viola, were always grouped together. This observation indicates a similar chemical composition.…”

Pressurized liquid extraction and GC‐MS analysis for simultaneous determination of seven components in Cinnamomum cassia and the effect of sample preparation

“…In addition, sample preparation is the crucial first step in the analysis of herbs and roughly accounts for approximately 30% of the analytical error [34]. To date, though hydrodistillation [30,32], ultrasonication [25,26], mechanical shaking [5] with different solvent, including methanol, ethanol, water or their mixed solutions in different ratio, have been used for sample preparation of C. cassia [35], pressurized liquid extraction (PLE), an innovative sample preparation technique which combines elevated temperature and high pressure to achieve fast and efficient extraction of the analytes from the (semi-)solid matrices, has not been employed in chemical analysis of CC.…”

“…Up to date, TLC [24], HPLC [5,[25][26][27], GC [4,28,29] and GC-MS [30][31][32][33] have been developed for analysis of different species of Cinnamomum. However, these methods focused on the qualitative analysis [25,27,[29][30][31][32] or quantitative determination of few components [26,28,33], due to the lack of reference compounds. In addition, sample preparation is the crucial first step in the analysis of herbs and roughly accounts for approximately 30% of the analytical error [34].…”