JOURNAL ARTICLE
Comprehensive Identification of Chemical Fingerprint and Screening of Potential Quality Markers of Aloe vera (L.) Burm. f. from Different Geographical Origins via Ultra-High-Performance Liquid Chromatography Hyphenated with Quadrupole–Orbitrap-High-Resolution Mass Spectrometry Combined with Chemometrics
Published In: Journal of Chromatographic Science, 2023, v. 61, n. 4. P. 312 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Yan, Yahui; Abdulla, Rahima; Ma, Qingling; Aisa, Haji Akber 3 of 3
Abstract
The article focuses on developing a comprehensive chemical fingerprinting and chemometric analysis strategy for quality assessment of Aloe vera (L.) Burm. f. from different geographical origins in China. Using ultra-high-performance liquid chromatography (UHPLC) coupled with quadrupole–orbitrap high-resolution mass spectrometry (Q–Orbitrap–HRMS), 32 common chemical peaks were identified across 20 batches from four provinces, and chemometric methods—including hierarchical cluster analysis (HCA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA)—classified samples into four geographic clusters. Five compounds—aloesin, aloin A, aloin B, aloeresin D, and 7-O-methylaloeresin A—were identified as potential quality markers, with their contents varying significantly by region, suggesting geographical origin influences Aloe vera quality. The study establishes an efficient analytical approach for screening quality markers and supports the formulation of quality standards for Aloe vera and its preparations.
Additional Information
- Source:Journal of Chromatographic Science. 2023/04, Vol. 61, Issue 4, p312
- Document Type:Article
- Subject Area:Complementary and Alternative Medicine
- Publication Date:2023
- ISSN:0021-9665
- DOI:10.1093/chromsci/bmad009
- Accession Number:163318249
- 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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