Development and Implementation of Automated Qualification Processes for the Identification of Pollutants in an Aquatic Environment from High-Resolution Mass Spectrometric Nontarget Screening Data

Abstract

Environmental pollution monitoring represents a major challenge due to the growing presence of a large and diverse number of potential contaminants. In complement to target analysis, nontarget analysis, via liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS), is increasingly used to provide a more comprehensive characterization of pollution. The challenge associated with this type of analysis is particularly related to the data treatment for substance identification. One of the main limitations is that all data must be manually reviewed, which is tedious and time-consuming. Machine learning algorithms aim to reproduce human behavior, and their capabilities were therefore evaluated to automatically identify substances in suspect screening approaches. After selecting the relevant features produced from LC/HRMS, seven different machine learning models were evaluated for each of the three different databases, which resulted in the selection of logistic regression (LR) and random forest (RF)-based algorithms. An interface was built to rank the identified substances and to assess the performance of the developed models. The LR model provided the best results when retention times were available. The developed LR and RF models were determined complementarily, particularly when no retention times were available. However, limitations were noticed when using a database containing different HRMS technologies.

Partially saturated vertical surface flow constructed wetland for emerging contaminants and antibiotic resistance genes removal from wastewater: The effect of bioaugmentation with Trichoderma

“…MS 2 and data-independent acquisitions (DIA) were achieved at a resolution of 17,500, two absolute collision energies (20 eV and 40 eV) isolation window 1 m/z, AGC of 5 × 10 4 , 150 ms of the maximum ion injection time, and a scan range of 50 -1000 m/z. The nontarget screening was performed with a home-made tool (Lestremau et al, 2023). The tool was developed using a machine learning approach aiming at improving the ranking of compounds identification.…”

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

“…PubChemLite, a subset of PubChem, was introduced by Schymanski et al in 2020 for more efficient identification efforts in exposomics and environmental research [7]. Recent progress has been made regarding improving chemical identifications through integrating experimental spectra (ENTACT) into MassBank and developing automated workflows to support non-targeted exposomics and NTS surveys on aquatic samples [57,58]. A further improvement may be achieved by adding spectral data measured over different types of HRMS systems under varying conditions.…”

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion

“…In an HRMS instrument, MS/MS can improve selectivity in annotation efforts, as MS/MS offers more annotation selectivity than does accurate mass alone. However, the search for unknowns in MS/MS or in-source fragment ion libraries is limited to the recorded spectra of reference standards, which is not sufficient for a real unknown screening and suffers from limited comparability among instruments [ 4 ]. Therefore an in silico strategy for determining unknown chemical structures by matching measured with quantitative structure–activity relationship (QSAR)-based predicted patterns from chemical databases is an alternative approach [ 5 ].…”