Jonathan Zweigle, Selina Tisler, Marta Bevilacqua, Giorgio Tomasi, Nikoline J. Nielsen, Nadine Gawlitta, Josephine S. Lübeck, Age K. Smilde, Jan H. Christensen
{"title":"用色谱法对环境样品进行非目标筛选的优先策略。高分辨率质谱法:教程","authors":"Jonathan Zweigle, Selina Tisler, Marta Bevilacqua, Giorgio Tomasi, Nikoline J. Nielsen, Nadine Gawlitta, Josephine S. Lübeck, Age K. Smilde, Jan H. Christensen","doi":"10.1016/j.chroma.2025.465944","DOIUrl":null,"url":null,"abstract":"<div><div>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 (<em>m/z</em>, 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.</div><div>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.</div><div>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.</div></div>","PeriodicalId":347,"journal":{"name":"Journal of Chromatography A","volume":"1751 ","pages":"Article 465944"},"PeriodicalIF":3.8000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prioritization strategies for non-target screening in environmental samples by chromatography – High-resolution mass spectrometry: A tutorial\",\"authors\":\"Jonathan Zweigle, Selina Tisler, Marta Bevilacqua, Giorgio Tomasi, Nikoline J. 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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.
期刊介绍:
The Journal of Chromatography A provides a forum for the publication of original research and critical reviews on all aspects of fundamental and applied separation science. The scope of the journal includes chromatography and related techniques, electromigration techniques (e.g. electrophoresis, electrochromatography), hyphenated and other multi-dimensional techniques, sample preparation, and detection methods such as mass spectrometry. Contributions consist mainly of research papers dealing with the theory of separation methods, instrumental developments and analytical and preparative applications of general interest.