JOURNAL ARTICLE
Authenticity study of commercial samples of St. John's wort by paper spray ionization mass spectrometry and chemometric tools.
Published In: Journal of Mass Spectrometry, 2023, v. 58, n. 7. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Miguita, Ana Gabriella Carvalho; Augusti, Rodinei; Sena, Marcelo Martins; Nascentes, Clésia Cristina 3 of 3
Abstract
Hypericum perforatum L. (St. John's wort) is one of the world's most consumed medicinal plants for treating depression and psychiatric disorders. Counterfeiting can occur in the medicinal plant trade, either due to the lack of active ingredients or the addition of substances not mentioned on the labels, often without therapeutic value or even harmful to health. Hence, 43 samples of St. John's wort commercially acquired in different Brazilian regions and other countries were analyzed by paper spray ionization mass spectrometry (PS‐MS) and modeled by principal component analysis. Hence, samples (plants, capsules, and tablets) were extracted with ethanol in a solid–liquid extraction. For the first time, PS‐MS analysis allowed the detection of counterfeit H. perforatum samples containing active principles typical of other plants, such as Ageratum conyzoides and Senna spectabilis. About 52.3% of the samples were considered adulterated for having at least one of these two species in their composition. Furthermore, out of 35 samples produced in Brazil, only 13 were deemed authentic, having only H. perforatum. Therefore, there is a clear need to improve these drugs' quality control in Brazil. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Mass Spectrometry. 2023/07, Vol. 58, Issue 7, p1
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:1076-5174
- DOI:10.1002/jms.4960
- Accession Number:165047191
- Copyright Statement:Copyright of Journal of Mass Spectrometry is the property of Wiley-Blackwell 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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