High resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) are advancing the identification of emerging contaminants in environmental and agricultural matrices. However, confidence in structure identification of unknowns in NTA presents challenges to analytical chemists. Structure identification requires integration of complementary data types such as reference databases, fragmentation prediction tools, and retention time prediction models. The goal of this research is to optimize and implement structure identification functionality within the US EPA’s CompTox Chemicals Dashboard, an open chemistry resource and web application containing data for ~760,000 substances. Rank-ordering the number of sources associated with chemical records within the Dashboard (Data Source Ranking) improves the identification of unknowns by bringing the most likely candidate structures to the top of a search results list. Incorporating additional data streams contained within the database underlying the Dashboard further enhances identifications. Integrating tandem mass spectrometry data into NTA workflows enables spectral match scores and increases confidence in structural assignments. We have generated and stored predicted MS/MS fragmentation spectra for the entirety of the Chemicals Dashboard using the in silico prediction tool CFM-ID. Predicted fragments incorporated into the identification workflow were used as both a scoring term and as a candidate threshold cutoff. Combining these steps within an open chemistry resource provides a freely available software tool for structure identification and NTA. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

Consensus ranking and fragmentation prediction for identification of unknowns in high resolution mass spectrometry

2019 Spring National Meeting

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