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Wiley, Journal of Separation Science, 10(45), p. 1727-1736, 2022

DOI: 10.1002/jssc.202200013

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Comprehensive profiling of Sanguisorba officinalis using offline two‐dimensional mixed‐mode liquid chromatography × reversed‐phase liquid chromatography, tandem high‐resolution mass spectrometry, and molecular network

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

The profiling of natural products is important in modern biological sciences and new drug development. However, the separation and characterization of complex herbal extracts are significantly challenging for researchers in the biochemical field. Herein, an offline two‐dimensional mixed‐mode liquid chromatography × reversed‐phase liquid chromatography system is developed. Our system exhibits high orthogonality and is composed of a newly prepared stationary phase in the first dimension and a traditional C18 phase in the second dimension, and is operated in combination with a high‐resolution mass spectrometry and molecular network. Sanguisorba officinalis L. is studied using the proposed method owing to its bioactivity. With the aid of orthogonal separation, the ionization of the individual components is improved. The number of detected compounds and separated peaks are significantly increased when one‐dimensional liquid chromatography is upgraded to two‐dimensional liquid chromatography. In addition, 270 compounds (127 of which are tentatively characterized as new compounds, and further confirmation is needed) are successfully characterized based on their fragmentation patterns under the guidance of molecular network, while only 95 compounds are characterized using one‐dimensional liquid chromatography and high‐resolution mass spectrometry. The results indicate that the developed offline two‐dimensional mixed‐mode liquid chromatography × reversed‐phase liquid chromatography, tandem high‐resolution mass spectrometry, and molecular network method are effective for profiling complex samples.