دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives
المؤلفون: Vosough, Maryam, Schmidt, Torsten C., Renner, Gerrit
المساهمون: Universität Duisburg-Essen
المصدر: Analytical and Bioanalytical Chemistry ; ISSN 1618-2642 1618-2650
بيانات النشر: Springer Science and Business Media LLC
سنة النشر: 2024
مصطلحات موضوعية: Biochemistry, Analytical Chemistry
الوصف: This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS’s role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.
نوع الوثيقة: article in journal/newspaper
اللغة: English
DOI: 10.1007/s00216-024-05153-8
DOI: 10.1007/s00216-024-05153-8.pdf
DOI: 10.1007/s00216-024-05153-8/fulltext.html
الإتاحة: https://doi.org/10.1007/s00216-024-05153-8Test
حقوق: https://creativecommons.org/licenses/by/4.0Test ; https://creativecommons.org/licenses/by/4.0Test
رقم الانضمام: edsbas.FC439269
قاعدة البيانات: BASE