1. Improving Target and Suspect Screening High-Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation
- Author
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Jeff Goshawk, Juan V. Sancho, Alberto Celma, Emma L. Schymanski, Lubertus Bijlsma, Félix Hernández, Gitte Barknowitz, María Ibáñez, and David Fabregat-Safont
- Subjects
Environmental analysis ,Computer science ,Electrospray ionization ,General Chemistry ,010501 environmental sciences ,Mass spectrometry ,computer.software_genre ,01 natural sciences ,Mass Spectrometry ,Ion ,Workflow ,Molecular Weight ,Identification (information) ,Chromatographic separation ,Ion Mobility Spectrometry ,Environmental Chemistry ,Data mining ,computer ,Identification criteria ,0105 earth and related environmental sciences - Abstract
Currently, the most powerful approach to monitor organic micropollutants (OMPs) in environmental samples is the combination of target, suspect, and nontarget screening strategies using high-resolution mass spectrometry (HRMS). However, the high complexity of sample matrices and the huge number of OMPs potentially present in samples at low concentrations pose an analytical challenge. Ion mobility separation (IMS) combined with HRMS instruments (IMS−HRMS) introduces an additional analytical dimension, providing extra information, which facilitates the identification of OMPs. The collision cross-section (CCS) value provided by IMS is unaffected by the matrix or chromatographic separation. Consequently, the creation of CCS databases and the inclusion of ion mobility within identification criteria are of high interest for an enhanced and robust screening strategy. In this work, a CCS library for IMS−HRMS, which is online and freely available, was developed for 556 OMPs in both positive and negative ionization modes using electrospray ionization. The inclusion of ion mobility data in widely adopted confidence levels for identification in environmental reporting is discussed. Illustrative examples of OMPs found in environmental samples are presented to highlight the potential of IMS−HRMS and to demonstrate the additional value of CCS data in various screening strategies.
- Published
- 2020