Yang J, Li W, Ding J, Dong Y, Xie X, Zhao F, Pan J, Qu H. A
multivariate curve resolution-alternating least squares (MCR-ALS) technology assisted
1 H-NMR methodology for multi-component quantitation of Trichosanthis Pericarpium injection.
Phytochem Anal 2023;
34:40-47. [PMID:
36278832 DOI:
10.1002/pca.3177]
[Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/07/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
INTRODUCTION
Trichosanthis Pericarpium injection (TPI) is a traditional Chinese medicine preparation obtained from Trichosanthis Pericarpium by extraction, purification and sterilisation. It contains amino acids, alkaloids, nucleotides and other components. Existing quantitative methods only analyse a few components in injections, so this study intends to develop a method for comprehensive analysis of TPI components.
OBJECTIVE
To develop a method for quantification of components in TPI by multivariate curve resolution-alternating least squares (MCR-ALS) assisted proton nuclear magnetic resonance (1 H-NMR).
METHODS
A 1 H-NMR method was developed for the quantification of components in TPI. For components with independent signals, 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (TSP) was used as an internal standard to calculate the component contents. For components with overlapping signals, the method of MCR-ALS was used.
RESULTS
A total of 36 components were identified in TPI, of which 33 were quantified. Methodological validation results showed that the developed 1 H-NMR method has good linearity, accuracy, precision, robustness and specificity.
CONCLUSION
The use of 1 H-NMR provides a reliable and universal method for the TPI components identification and quantification. Also, it can be used as a powerful tool for analysing the contents in a complex mixture as a quality control measure.
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