Abstract
Non-target screening (NTS) based on the combination of liquid chromatography coupled to high-resolution mass spectrometry has become the key method to identify organic micro-pollutants (OMPs) in water samples. However, a large number of compounds remains unidentified with current NTS approaches due to poor quality fragmentation spectra generated by suboptimal fragmentation methods. Here, the potential of the alternative fragmentation technique ultraviolet photodissociation (UVPD) to improve identification of OMPs in water samples was investigated. A diverse set of water-relevant OMPs was selected based on k-means clustering and unsupervised artificial neural networks. The selected OMPs were analyzed using an Orbitrap Fusion Lumos equipped with UVPD. Therewith, information-rich MS2 fragmentation spectra of compounds that fragment poorly with higher-energy collisional dissociation (HCD) could be attained. Development of an R-based data analysis workflow and user interface facilitated the characterization and comparison of HCD and UVPD fragmentation patterns. UVPD and HCD generated both unique and common fragments, demonstrating that some fragmentation pathways are specific to the respective fragmentation method, while others seem more generic. Application of UVPD fragmentation to the analysis of surface water enabled OMP identification using existing HCD spectral libraries. However, high-throughput applications still require optimization of informatics workflows and spectral libraries tailored to UVPD.
Highlights
As little was known about ultraviolet photodissociation (UVPD) fragmentation pathways of these organic micro-pollutants (OMPs), we applied the combinatorial prediction algorithm of MetFrag [7], which does not rely on fragmentation rules, but uses a bond dissociation approach to predict potential fragments and matches these to the experimentally observed
Combining the novel fragmentation technique UVPD and cheminformatics tools, we showed the potential of UVPD for structural elucidation of water-relevant OMPs in Non-target screening (NTS) data
Based on the two complementary methods k-means clustering and self-organizing map (SOM), a set of OMPs could be selected that was representative for the water cycle and a wide chemical space
Summary
Reliable identification of organic micro-pollutants (OMPs) in drinking water and its sources is essential to risk assessment and prediction of the behavior of a substance in the environment and during water treatment. OMPs in water samples, as it has the potential to detect all ionizable compounds that are amenable to the selected chromatographic separation, within a defined mass range [1]. The unambiguous identification of an OMP from NTS data relies on the accurate mass and isotopic pattern from the full scan MS1 spectrum to determine the elemental formula of the compound. The MS2-based structural identification typically relies on matching of the experimental spectrum with entries in spectral libraries or in silico predicted fragmentation spectra. For compounds that show poor fragmentation spectra generated by higher-energy collisional dissociation (HCD) fragmentation, the fragmentation technique routinely applied in Orbitrap based NTS, confident structural elucidation often remains elusive
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