JOURNAL ARTICLE
An High Performance Thin Layer Chromatography (HPTLC) Validated Method for the Determination of Ephedrine Alkaloids in Multi-Plant Formulations.
Published In: Journal of Chromatographic Science, 2025, v. 63, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Gemma, Simonetta; Multari, Giuseppina; Gallo, Francesca R 3 of 3
Abstract
This article focuses on the development and validation of a High Performance Thin Layer Chromatography (HPTLC) densitometry method for the rapid qualitative and quantitative detection of ephedrine alkaloids, primarily ephedrine and pseudoephedrine, in multi-plant formulations and Ephedra species such as Ephedra sinica Stapf. The method uses a methanol/water extraction and a mobile phase of ammonia/methanol/dichloromethane, with ninhydrin derivatization enabling specific detection at 500 nm, allowing simultaneous screening of up to 19 samples within 45 minutes. Validated according to ICH guidelines, the technique demonstrated high specificity, sensitivity (limit of detection 0.0020 μg/band), accuracy (average recovery ~89%), and robustness, making it suitable for routine quality control and rapid screening of ephedrine-containing products, including those seized for illegal distribution. Given the regulatory restrictions on ephedrine due to its use in illicit methamphetamine production, this cost-effective and efficient method offers practical utility for monitoring ephedrine alkaloids in herbal supplements and related products.
Additional Information
- Source:Journal of Chromatographic Science. 2025/04, Vol. 63, Issue 4, p1
- Document Type:Article
- Subject Area:Complementary and Alternative Medicine
- Publication Date:2025
- ISSN:0021-9665
- DOI:10.1093/chromsci/bmaf020
- Accession Number:185428178
- Copyright Statement:Copyright of Journal of Chromatographic Science is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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