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

Abstract

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.

Keywords: Aquatic contaminants; Chemometrics/machine learning; Data standardization; High-resolution mass spectrometry; Non-target screening; QA/QC in water analysis; Quantitative non-target screening.

Fig. 1

Representation of ASCA(+) loading plot (Factor “sampling location”) to display the final subsets of highly prioritized chemicals (ESI+) strongly associated with different stream water sources by the joint implementation of univariate (volcano test) and multivariate statistical methods (PLS-DA-VIP and ASCA+) [28]. Note: ASCA(+): extension of ASCA (ANOVA Simultaneous Component Analysis) for unbalanced experimental design; PLS-DA-VIP: Partial Least Squares-Discriminant Analysis-Variable Importance in Projection

Fig. 2

Overview of the essential elements of QA/QC for NTS in water analysis

Fig. 3

Schematic representation of the emerging NTS-Omics integration framework within an aquatic monitoring and ecosystem health study. Note: PPCPs: Pharmaceuticals and Personal Care Products; TPs: Transformation Products; PFAS: Perfluoroalkyl and Polyfluoroalky Substances; DBP: Disinfection Byproducts; eDNA: environmental DNA