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

IF 3.8 2区 化学 Q1 BIOCHEMICAL RESEARCH METHODS
Jonathan Zweigle, Selina Tisler, Marta Bevilacqua, Giorgio Tomasi, Nikoline J. Nielsen, Nadine Gawlitta, Josephine S. Lübeck, Age K. Smilde, Jan H. Christensen
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引用次数: 0

摘要

利用色谱耦合高分辨率质谱(HRMS)的非靶标筛选(NTS)已经成为复杂环境基质中检测和优先考虑新兴关注化学物质(CECs)的基础。产生的大量特征(m/z,保留时间和强度)需要有效的优先级策略来确定环境和毒理学相关的CECs。由于复合识别仍然是NTS的主要瓶颈,因此确定优先级对于将识别工作集中在最重要的地方至关重要。本教程介绍了七种优先排序策略:(1)使用参考文库筛选已知或可疑化合物的目标和可疑化合物。(2)数据质量滤波,采用质量控制措施,降低噪声和误报数量。(3)利用HRMS数据属性对特定化合物类别(如卤化物质、转化产物)进行化学驱动的优先排序。(4)过程驱动——使用空间、时间或基于过程的比较(技术前和技术后过程)来识别关键特征。(5)效应导向分析(EDA)和虚拟效应导向分析(vEDA)优先将化学特征与生物效应联系起来。(6)基于预测的优先排序,如定量结构-性质关系(QSPR)和机器学习,以估计风险或浓度水平;(7)基于像素或瓦片的分析,其中色谱图像(2D数据)用于确定感兴趣的区域或用于更大样本集的比较。通过整合这些优先级策略,本教程提供了一个结构化的基础,以评估已识别和未识别的特征,优先考虑高风险化合物,并推进环境风险评估和监管决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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来源期刊
Journal of Chromatography A
Journal of Chromatography A 化学-分析化学
CiteScore
7.90
自引率
14.60%
发文量
742
审稿时长
45 days
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.
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