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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.

Ultraviolet Photodissociation for Non-Target Screening-Based Identification of Organic Micro-Pollutants in Water Samples