Abstract
ABSTRACT Non-targeted analysis (NTA) methods have been increasingly used to discover organic contaminants in environmental samples. The identification efficiency of conventional NTA workflow is often very low due to many factors such as low fragmentation score between experimental MS2 data and reference MS2 data/in-silico MS2 data. Additionally, the prioritisation of possible candidates is also challenging due to the large number of candidates obtained from MS acquisitions. In this study, a multiplatform data processing workflow applicable to non-targeted LC-MS data is proposed to improve the identification accuracy of organic contaminants in water. This workflow consists of (1) scanning quadrupole data-independent acquisition (SONAR) to acquire relatively clean MS1 and MS2 data, (2) data deconvolution using Progenesis QI Software to filter/select the appropriate ions (MS1 data), (3) the selected MS1 data matching against U.S. EPA Chemistry Dashboard to get a list of the environmentally related candidates with metadata (e.g., data sources, toxicity, and exposure), (4) experimental MS2 data matching against in-silico fragmentation data of the obtained candidate by MetFrag to obtain a fragmentation score, (5) compound identification through ranking the candidates by MetFrag based on fragmentation score and metadata. The identification efficiency of the metadata-based NTA workflow was evaluated using a 33-component mixture of chemicals in a complex sludge water. Out of 33 spiked compounds, 26 compounds had been identified using the defined NTA workflow. Most of the 26 identified compounds were recognized as 1st hit of the candidates obtained from the library searching. Overall, the incorporation of fragmentation score and metadata to rank the candidates has significantly improved the accuracy of the organic contaminant identification in the sludge water, compared to the conventional NTA workflow based solely on the fragmentation score.
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