Prioritization strategies for non-target screening in environmental samples by chromatography – High-resolution mass spectrometry: A tutorial

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS), has become fundamental for detecting and prioritizing chemicals of emerging concern (CECs) in complex environmental matrices. The vast number of generated features (m/z, retention time, and intensity) necessitate effective prioritization strategies to identify environmentally and toxicologically relevant CECs. Since compound identification remains a major bottleneck in NTS, prioritization is critical to focus identification efforts where they matter most. This tutorial presents seven prioritization strategies: (1) Target and suspect screening for identifying known or suspected compounds using reference libraries. (2) Data quality filtering to apply quality control measures to reduce noise and the number of false positives. (3) Chemistry-driven prioritization using HRMS data properties to prioritize specific compound classes (e.g., halogenated substances, transformation products). (4) Process-driven - using spatial, temporal, or process-based comparisons (pre- and post-technical processes) to identify key features. (5) Effect-Directed Analysis (EDA) and Virtual Effect-Directed Analysis (vEDA) prioritization to link chemical features to biological effects. (6) Prediction-based prioritization such as quantitative structure-property relationships (QSPR) and machine learning to estimate risk or concentration levels, and (7) Pixel- or tile-based analysis where the chromatographic image (2D data) is used to pin-point regions of interest or for comparison of larger sample sets. By integrating these prioritization strategies, this tutorial provides a structured foundation to evaluate both identified and unidentified features, prioritize high-risk compounds, and advance environmental risk assessment and regulatory decision-making.

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Publication Details

Journal
Journal of Chromatography A
Published
2025-04-06
DOI
https://doi.org/10.1016/j.chroma.2025.465944
Citations
9
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
7.25

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article

Prioritization strategies for non-target screening in environmental samples by chromatography – High-resolution mass spectrometry: A tutorial

Jan H. Christensen, Nadine Gawlitta, Josephine S. Lübeck, Selina Tisler et al.
9 citations
Journal of Chromatography A
Metabolomics and Mass Spectrometry Studies
7.25
article

Prioritization strategies for non-target screening in environmental samples by chromatography – High-resolution mass spectrometry: A tutorial

Jan H. Christensen, Nadine Gawlitta, Josephine S. Lübeck, Selina Tisler, Giorgio Tomasi, Marta Bevilacqua, Jonathan Zweigle, Nikoline Juul Nielsen, Age K. Smilde
article en
9 citations

Abstract

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS), has become fundamental for detecting and prioritizing chemicals of emerging concern (CECs) in complex environmental matrices. The vast number of generated features (m/z, retention time, and intensity) necessitate effective prioritization strategies to identify environmentally and toxicologically relevant CECs. Since compound identification remains a major bottleneck in NTS, prioritization is critical to focus identification efforts where they matter most. This tutorial presents seven prioritization strategies: (1) Target and suspect screening for identifying known or suspected compounds using reference libraries. (2) Data quality filtering to apply quality control measures to reduce noise and the number of false positives. (3) Chemistry-driven prioritization using HRMS data properties to prioritize specific compound classes (e.g., halogenated substances, transformation products). (4) Process-driven - using spatial, temporal, or process-based comparisons (pre- and post-technical processes) to identify key features. (5) Effect-Directed Analysis (EDA) and Virtual Effect-Directed Analysis (vEDA) prioritization to link chemical features to biological effects. (6) Prediction-based prioritization such as quantitative structure-property relationships (QSPR) and machine learning to estimate risk or concentration levels, and (7) Pixel- or tile-based analysis where the chromatographic image (2D data) is used to pin-point regions of interest or for comparison of larger sample sets. By integrating these prioritization strategies, this tutorial provides a structured foundation to evaluate both identified and unidentified features, prioritize high-risk compounds, and advance environmental risk assessment and regulatory decision-making.

Journal of Chromatography AVol. 1751
University of Copenhagen (DK)
Innovationsfonden, European Commission, HORIZON EUROPE Framework Programme
Climate action
Openalex Percentile: Top 3%
Metabolomics and Mass Spectrometry Studies
7.25
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