Issue 47, 2023

Determination of total phenol and six polyphenolic components in the polyphenol extract of Cinnamomi cortex by quantitative nuclear magnetic resonance spectroscopy

Abstract

A quantitative nuclear magnetic resonance spectroscopy (qNMR) method was established for determining the total phenol and six polyphenolic components in the polyphenol extract of Cinnamomi cortex. The qNMR approach utilized DMSO-d6 as the deuterated solvent and potassium hydrogen phthalate as the internal standard for quantifying the total phenolic content, expressed as epicatechin equivalence in the sample. Two complementary qNMR methods with DMSO-d6 or D2O as solvent were established to simultaneously determine 6 polyphenol components in the cinnamon polyphenol extract, including epigallocatechin gallate (EGCG), epicatechingallate (ECG), epicatechin (EC), epigallocatechin (EGC), gallocatechin gallate (GCG) and gallic acid (GA). Method validation demonstrated excellent precision with intraday relative standard deviation (RSD) below 1.08% and interday RSD below 1.48%. The linear correlation coefficient (r) exceeded 0.999, and the limits of detection (LOD) were from 0.01 to 0.14 mg mL−1, while the limits of quantification (LOQ) were from 0.07 to 0.69 mg mL−1. Recovery rates for this method fell within the range of 98.2% to 101.7%. Furthermore, the method has been successfully applied for determining the polyphenolic content in authentic cinnamon polyphenol extracts obtained from different sources.

Graphical abstract: Determination of total phenol and six polyphenolic components in the polyphenol extract of Cinnamomi cortex by quantitative nuclear magnetic resonance spectroscopy

Supplementary files

Article information

Article type
Paper
Submitted
28 Aug 2023
Accepted
09 Nov 2023
First published
13 Nov 2023

Anal. Methods, 2023,15, 6561-6570

Determination of total phenol and six polyphenolic components in the polyphenol extract of Cinnamomi cortex by quantitative nuclear magnetic resonance spectroscopy

D. Shi, Y. Zheng, Q. Guo, C. Gong, X. Xu and J. Gao, Anal. Methods, 2023, 15, 6561 DOI: 10.1039/D3AY01501G

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