LC-HRMS Data Processing Strategy for Reliable Sample Comparison Exemplified by the Assessment of Water Treatment Processes

The behavior of micropollutants in water treatment is an important aspect in terms of water quality. Nontarget screening by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) offers the opportunity to comprehensively assess water treatment processes by comparing the signal heights of all detectable compounds before and after treatment. Without preselection of known target compounds, all accessible information is used to describe changes across processes and thus serves as a measure for the treatment efficiency. In this study, we introduce a novel LC-HRMS data processing strategy for the reliable classification of signals based on the observed fold changes. An approach for filtering detected features was developed and, after parameter adjustment, validated for its recall and precision. As proof of concept, the fate of 411 target compounds in a 0.1 μg/L standard mix was tracked throughout the data processing stages, where 406 targets were successfully recognized and retained during filtering. Potential pitfalls in signal classification were addressed. We found the recursive peak integration to be a key point for the reliable classification of signal changes across a process. For evaluating the repeatability, a combinatorial approach was conducted to verify the consistency of the final outcome using technical replicates of influent and effluent samples taken from an ozonation process during drinking water treatment. The results showed sufficient repeatability and thus emphasized the applicability of nontarget screening for the assessment of water treatment processes. The developed data processing strategies may be transferred to other research fields where sample comparisons are conducted.