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In HPLC, two phases play a critical role in the separation process:

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A new PARAFAC-based algorithm for HPLC-MS data treatment: herbal extracts identification.

Polina Turova¹, Igor Rodin¹, Oleg Shpigun¹

¹Chemistry Department, Lomonosov Moscow State University, Moscow, Russia.

Phytochemical Analysis : PCA

|June 20, 2020

Summary

A new HPLC-MS-PARAFAC method efficiently identifies plant extracts in complex mixtures like herbal products. This approach aids in quality control by analyzing large datasets without commercial standards.

Keywords:

HPLC-MS PARAFAC chemometrics herbal extracts quality control

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Area of Science:

Analytical Chemistry
Chemometrics
Pharmacognosy

Background:

High-performance liquid chromatography-mass spectrometry (HPLC-MS) is crucial for quality control, especially for complex herbal products with multiple plant extracts.
Handling large datasets from such analyses is challenging due to the lack of commercial standards.
Novel approaches are needed for comprehensive data treatment and identification of plant-derived components.

Purpose of the Study:

To develop a straightforward and information-saving algorithm for identifying plant extracts in commercial products.
To enable accurate analysis of complex mixtures without relying on commercial standards.
To improve the efficiency of quality control for herbal formulations.

Main Methods:

Analysis of 34 samples including Glycyrrhiza glabra, Panax ginseng, Abrus precatorius, their mixtures, and flavored oolong tea using HPLC-MS.
Creation of a three-dimensional dataset (retention time-mass-to-charge ratio-samples).
Application of smoothing, denoising, and parallel factor analysis (PARAFAC) for data decomposition.

Main Results:

Samples were successfully clustered into eight groups.
Characteristic triterpene glycoside groups were identified, confirming the presence of Abrus precatorius and G. glabra in flavored tea.
The characteristic chromatogram approach supported the findings.

Conclusions:

The developed HPLC-MS-PARAFAC method is a reliable and efficient tool for analyzing complex mixtures.
This method effectively handles large, untreated experimental data.
Future development could enhance the chemical composition evaluation and quality control of food additives and other complex formulations.