Issue 46, 2018

Chemometrics-assisted HPLC-DAD as a rapid and interference-free strategy for simultaneous determination of 17 polyphenols in raw propolis

Abstract

A methodology for simultaneously measuring seventeen polyphenols in six Chinese raw propolis samples by using chemometrics-assisted high performance liquid chromatography-diode array detection (HPLC-DAD) was developed and validated. With the aid of a second-order calibration method based on an alternating trilinear decomposition (ATLD) algorithm, HPLC analysis was easily achieved within 16.5 min even in the presence of heavily overlapped peaks and unknown interferents. The average recoveries were in the range of 95.2% to 118.7% with standard deviation less than 7.0% for the selected raw propolis. Figures of merit including sensitivity (SEN), selectivity (SEL), root mean square errors of prediction (RMSEPs), etc. were calculated to validate the accuracy of the proposed method. For further confirmation, a multiple reaction monitoring (MRM) method based on LC-MS/MS was adopted. The paired F-test and t-test results showed that there was no significant difference between the MRM and the proposed method at the 95% confidence level, which indicated that the developed ATLD-assisted HPLC-DAD method is a suitable tool for the rapid analysis of Chinese raw propolis. Considering its advantages of simplicity, rapidity, precision and accuracy, it might also be extended as an alternative and available method for the quality monitoring of propolis raw material in the pharmaceutical industry.

Graphical abstract: Chemometrics-assisted HPLC-DAD as a rapid and interference-free strategy for simultaneous determination of 17 polyphenols in raw propolis

Supplementary files

Article information

Article type
Paper
Submitted
12 Sep 2018
Accepted
06 Nov 2018
First published
08 Nov 2018

Anal. Methods, 2018,10, 5577-5588

Chemometrics-assisted HPLC-DAD as a rapid and interference-free strategy for simultaneous determination of 17 polyphenols in raw propolis

Q. Liu, H. Wu, Z. Liu, R. Xiao, T. Wang, Y. Hu, Y. Ding and R. Yu, Anal. Methods, 2018, 10, 5577 DOI: 10.1039/C8AY01986J

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