Repository: Freie Universität Berlin, Math Department

High-performance liquid chromatography with electrospray ionization ion mobility spectrometry: Characterization, data management, and applications

Zühlke, Martin and Riebe, Daniel and Beitz, Toralf and Löhmannsröben, Hans-Gerd and Andreotti, Sandro and Reinert, Knut and Zenichowski, Karl and Diener, Marc (2016) High-performance liquid chromatography with electrospray ionization ion mobility spectrometry: Characterization, data management, and applications. Journal of Separation Science, 39 (24). pp. 4756-4764. ISSN 16159306

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Official URL: http://doi.org/10.1002/jssc.201600749

Abstract

The combination of high-performance liquid chromatography and electrospray ionization ion mobility spectrometry facilitates the two-dimensional separation of complex mixtures in the retention and drift time plane. The ion mobility spectrometer presented here was optimized for flow rates customarily used in high-performance liquid chromatography between 100 and 1500 μL/min. The characterization of the system with respect to such parameters as the peak capacity of each time dimension and of the 2D spectrum was carried out based on a separation of a pesticide mixture containing 24 substances. While the total ion current chromatogram is coarsely resolved, exhibiting coelutions for a number of compounds, all substances can be separately detected in the 2D plane due to the orthogonality of the separations in retention and drift dimensions. Another major advantage of the ion mobility detector is the identification of substances based on their characteristic mobilities. Electrospray ionization allows the detection of substances lacking a chromophore. As an example, the separation of a mixture of 18 amino acids is presented. A software built upon the free mass spectrometry package OpenMS was developed for processing the extensive 2D data. The different processing steps are implemented as separate modules which can be arranged in a graphic workflow facilitating automated processing of data.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:2128
Deposited By: Anja Kasseckert
Deposited On:22 Nov 2017 13:04
Last Modified:22 Nov 2017 13:19

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