Original ArticleIndian Journal of Pharmaceutical Education and ResearchVol. 57 | Issue 2 | 2023 | pp. 583–590Open access
Discrimination of Different Part of Curcuma longa by HPLC Fingerprints Combined with Multivariate Statistical Analysis
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- 2,
- 1,
- 1,
- 1,
- 1*
- 1 Department of New Energy Materials and Chemistry, Leshan Normal University, Leshan, CHINA.
- 2 Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu University, Chengdu, CHINA.
Published in Indian Journal of Pharmaceutical Education and Research
Correspondence: Kai Shi
Department of New Energy Materials and Chemistry, Leshan Normal University, Leshan, CHINA.
Email: shikai9901@163.com
Copyright: © 2023 Manuscript Technomedia. This is an open access article.
- Published:
- Mar 21, 2023
- Received:
- Mar 9, 2022
- Accepted:
- Jan 22, 2023
How to cite
Song, J., Xiang, D., Cheng, Y., Fang, Y., Wang, Y., & Shi, K. (2023). Discrimination of Different Part of Curcuma longa by HPLC Fingerprints Combined with Multivariate Statistical Analysis. Indian Journal of Pharmaceutical Education and Research, 57(2), 583–590. https://doi.org/10.5530/ijper.57.2.71
Abstract
Aim: The purpose of this study is to develop a method to explore the difference between the rhizomes and tuberous roots of Curcuma longa, and screen out the difference markers. Materials and Methods: The quantitative analysis and fingerprints of rhizomes and tuberous roots were established by HPLC, rhizomes and tuberous roots of Curcuma longa were clearly discriminated by Hierarchical Cluster Analysis (HCA), Similarity Analysis (SA) and Principal Component Analysis (PCA). The difference markers were screened out by Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Results: The contents of curcumin, bisdemethoxycurcumin and demethoxycurcumin in all rhizomes were higher than those in tuberous roots. Multivariate statistical analysis shown that the samples of rhizomes were grouped into the same categories and samples of tuberous root were grouped another group in each analysis mode. And the OPLS-DA model had a good productivity and good fit indicated by the value of R2Y=0.981, Q2=0.946and R2X=0.816. The important markers for discrimination samples were the peak 14, peak 10 (demethoxycurcumin) and peak 11 (curcumin). Conclusion: The fingerprinting combination of multivariate statistical analysis can be applied to distinguish the rhizomes and tuberous roots of Curcuma longa.
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Article metadata
| Title | Discrimination of Different Part of Curcuma longa by HPLC Fingerprints Combined with Multivariate Statistical Analysis |
|---|---|
| Authors | Jiuhua Song; Dabing Xiang; Ying Cheng; Yuan Fang; Yinghong Wang; Kai Shi |
| Affiliations | Department of New Energy Materials and Chemistry, Leshan Normal University, Leshan, CHINA.; Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu University, Chengdu, CHINA. |
| Corresponding author | shikai9901@163.com |
| Journal | Indian Journal of Pharmaceutical Education and Research |
| Volume / Issue | Vol. 57, Issue 2 (2023) |
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