JOURNAL ARTICLE

Development of Simultaneous HPTLC Method and Validation for the Quality Assessment of Ayurvedic Formulation—Ayush Kvatha Churna by Using Marker Compound Rosmarinic Acid, Trans-Cinnamaldehyde and Piperine.

  • Published In: Journal of Chromatographic Science, 2025, v. 63, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Sahu, Umakant; Chauhan, Nagendra Singh; Parihar, Arun Kumar Singh; Karbhal, Kamleshwar Singh; Inchulkar, Shrikant R; Gupta, Prashant Kumar; Singh, Rajesh Kumar 3 of 3

Abstract

The article focuses on the development and validation of a high-performance thin-layer chromatography (HPTLC) method for the identification and quantification of marker compounds—Rosmarinic acid, trans-Cinnamaldehyde, and Piperine—in Ayush Kvatha Churna (AKC), an Ayurvedic herbal formulation recommended by the Ministry of Ayush, Government of India, to boost immunity during the COVID-19 pandemic. The study analyzed multiple AKC samples using a solvent system of Toluene, Ethyl acetate, Ethanol, and Formic acid, confirming the presence and amounts of these markers with validated parameters including accuracy, precision, specificity, and limits of detection and quantification. The results demonstrated that the developed HPTLC method is simple, accurate, and reproducible, making it suitable for routine quality control and standardization of AKC formulations in research and testing laboratories.

Additional Information

  • Source:Journal of Chromatographic Science. 2025/03, Vol. 63, Issue 3, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0021-9665
  • DOI:10.1093/chromsci/bmae019
  • Accession Number:184405622
  • 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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