Data-independent acquisition with ion mobility mass spectrometry for suspect screening of per- and polyfluoroalkyl substances in environmental water samples.

Per- and polyfluoroalkyl substances (PFASs) have environmentally persistent, and the various types of PFASs have been detected in water environments. Many previous studies have performed data-dependent acquisition (DDA) of mass spectra from an environmental sample by high-resolution mass spectrometry to identify PFAS suspects individually. In comparison, the data-independent acquisition (DIA) of comprehensive mass spectra of the sample is a technology which enables to know the occurrences of suspects and non-targets simultaneously. However, it is difficult to associate the fragment ions of targeted precursor ions by retention time only, because of the existence of co-eluting ions derived from environmental samples. Since the separation of ions is not enough with only the conventional DIA method, here, we attempted to support it using ion mobility mass spectrometry (IMS) to distinguish the relevant ions from co-eluting ions by drift time. Firstly, suspect screening of PFASs with eternal database resulted in determining 32-96 PFAS suspects in firefighting foam impacted groundwater samples (n = 8) by suspect screening. Among all the pairs of respective precursor ions and fragment ions of the PFAS suspects, 5%-19% (4-9 PFASs) of them were associated without considering the drift time of IMS, while 37%-49% (15-43 PFASs) of them were associated with considering the drift time. The consideration of the drift time increased the association ratios in all samples. In a sample, most precursor ions could be associated with their fragment ions (41 of 43 PFASs) because at least one probable fragment ion was observed among three of maximum intensity fragment ions. Thus, the method improved the identification by excluding the unrelated co-eluting ions by IMS. Moreover, the method can acquire a certain reliable MS/MS spectra of suspects in environmental samples in one analysis. It is not essential to conduct instrumental analyses again for samples stored for a long time even when the data sets and/or methodologies of data analyses are modified (e.g., the original database, screening list, or statistical filtering/data cleaning approach). It will be particularly useful for studies that must analyze a large number of environmental samples.