1. Analysis of astragalosides I, II and IV in some Egyptian Astragalus species and Astragalus dietary supplements using high‐performance liquid chromatography/evaporative light scattering detector and non‐parametric regression
- Author
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Marwa S. Moneeb, Mohamed A. Korany, Alaa A. El-Banna, Nadia A. El-Sebakhy, and Aya M. Asaad
- Subjects
Chromatography ,biology ,Chemistry ,010401 analytical chemistry ,Plant Science ,General Medicine ,Derivative ,biology.organism_classification ,01 natural sciences ,Biochemistry ,High-performance liquid chromatography ,0104 chemical sciences ,Analytical Chemistry ,Nonparametric regression ,010404 medicinal & biomolecular chemistry ,Astragalus ,Complementary and alternative medicine ,Chromatography detector ,Standard addition ,Drug Discovery ,Molecular Medicine ,Quantitative analysis (chemistry) ,Food Science ,Second derivative - Abstract
Introduction GenuTs Astragalus L. is characterised by the presence of cycloartane saponins which have wide biological activities such as antioxidant, immunomodulating' hepatoprotective and anti-inflammatory activities. From these cycloartane saponins are astragalosides I, II and IV which have been regarded as the most important active constituents in Astragalus species. Objectives This work describes the quantitative analysis of astragalosides I, II and IV in some Egyptian Astragalus species and Astragalus dietary supplements in a single run by high-performance liquid chromatography/evaporative light scattering detector (HPLC/ELSD) using gradient elution. Methodology The method of quantitation adopted in this study is the standard addition method. First and second derivative treatment of the data was performed, and the study presents comparison between two statistical regression methods for handling data; parametric and non-parametric regression methods. Results Derivative treatment of the chromatographic response data gives improved quantitation of the chromatographic signals. Non-parametric regression of the data using Theil's method is advantageous over the usual least squares method as it assumes that errors could occur in both x- and y-directions and they might not be normally distributed. In addition, it could effectively circumvent any outlier data points. Conclusion Due to the simplicity and the good accuracy and reproducibility of the suggested methods, they could be used for analysis and quality control of Astragalus species and Astragalus dietary supplements.
- Published
- 2020
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