HPLC 2025 Preview: Prioritization Strategies in Non-Target Screening of Environmental Samples by Chromatography with High-Resolution Mass Spectrometry

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS) is a powerful tool in environmental analysis for detecting chemicals of emerging concern (CECs). The challenge lies in the large number of analytical features generated per sample, which requires prioritization to focus resources on the most relevant features. At HPLC2025, we present an overview of seven prioritization strategies: (1) target and suspect screening, (2) data quality filtering, (3) chemistry-driven prioritization, (4) process-driven prioritization, (5) effect-directed prioritization, (6) prediction-based prioritization, and (7) pixel- or tile-based chromatographic analyses. This article discusses how integrating these strategies can enhance compound identification, support environmental risk assessment, and accelerate decision-making.

As the anthropogenic environmental chemical space expands as a result of industrial activity and an increase in the diversity of consumer products, non-target screening (NTS) using chromatography–high-resolution mass spectrometry (HRMS) has become essential in environmental monitoring. However, the sheer number of detected features (mass-to-charge ratio [m/z], t_r pairs), often thousands for each sample, creates a bottleneck at the identification stage. Without an effective prioritization strategy, valuable time and resources are spent on irrelevant or redundant data.

This short article brings together the prioritization approaches that we will present at HPLC 2025 and in our recently published paper (1). The central message is that usually no single strategy is sufficient. Instead, combining strategies allows identification efforts to be focused where they matter most—enabling faster and improved assessments of chemical risk.

Figure 1: Seven prioritization strategies in non-target screening (NTS) by chromatography–HRMS, grouped by domain: chemical information (P1–P3), toxicological information (P5–P6), external information (P4), and preprocessing (P2, P7). Each strategy narrows the candidate space based on specific criteria. Combined, they reduce complexity and focus identification on environmentally relevant features.

Seven Prioritization Strategies (Figure 1)

1. Target and Suspect Screening (P1):

This strategy is based on predefined databases of known or suspected contaminants, such as those from PubChemLite, the CompTox Dashboard, or the NORMAN Suspect List Exchange. It narrows candidates early by matching features to compounds of known environmental relevance using m/z, isotope patterns, retention times, and MS/MS spectra. While able to reduce complexity, this approach is constrained by the completeness of databases and their quality.

2. Data Quality Filtering (P2):

Reliability-driven filtering removes artifacts and unreliable signals based on occurrence in blanks, replicate consistency, peak shape, or instrument drift. Though not sufficient for prioritization on its own, P2 is foundational. Without reliable data, identification becomes hypothetical. This step reduces false positives and improves accuracy and reproducibility.

3. Chemistry-Driven Prioritization (P3):

This strategy focuses on compound-specific properties useful to find certain compound classes of interest. Mass defect filtering identifies halogenated compounds such as per- and polyfluoroalkyl substances (PFAS). Homologue series detection, isotope patterns, and diagnostic MS/MS fragments help detect transformation products or homologues series. Multidimensional chromatography or ion mobility separation can be used here to increase specificity.

4. Process-Driven Prioritization (P4):

Prioritization is guided by spatial, temporal, or technical processes. Comparing influent and effluent samples from treatment plants, or upstream vs. downstream samples from river systems, highlights persistent or newly formed compounds. Correlation-based approaches link chemical signals to events like rainfall, or operational changes, while source-supported prioritization starts with known source signatures (plant toxins) and traces them into receiving environments (a river downstream from a field).

5. Effect-Directed Prioritization (P5):

Effect-directed analysis (EDA) integrates biological response data with chemical compositional data (chemical fingerprints). In traditional EDA, bioactive fractions are isolated and chemically analyzed. Virtual EDA (vEDA) links features to endpoints using statistical models (for example, partial least squares discriminant analysis) across multiple samples. This strategy directly targets bioactive contaminants and is useful when regulatory action depends on effect data.

6. Prediction-Based Prioritization (P6):

Combining predicted concentrations and toxicities allows calculation of risk quotients (PEC/PNEC, Predicted Environmental Concentration vs. Predicted No Effect Concentration). Models like MS2Quant predict concentrations directly from MS/MS spectra; MS2Tox estimates LC50 from fragment patterns. These tools are particularly useful when full identification is incomplete, but prioritization for risk is needed. The key advantage is focusing on substances of highest concern without full structural elucidation.

7. Pixel- and Tile-Based Approaches (P7):

For complex datasets (especially two-dimensional [2D] chromatography), feature-based analysis can be impractical. Pixel-based (two-dimensional gas chromatography [GC×GC], two-dimensional liquid chromatography LC×LC) and tile-based (rectangular RT´ RT windows) prioritization localizes regions of high variance or diagnostic power before peak detection. These strategies highlight chemically relevant regions for more elaborate analysis (peak curve resolution and compound detection) and are especially valuable in early-stage exploration or for large-scale monitoring.

Integrated Prioritization Strategies

Each strategy serves a different purpose in the NTS prioritization workflow (for example, P1 filters knowns, P5 links to biological effects, and P6 ranks features by predicted risk). When combined, they enable stepwise reduction from thousands of features to a focused shortlist. Figure 1 groups these strategies into four domains—chemical, toxicological, external, and preprocessing—each addressing a specific aspect of feature reduction.

For example, P1 may initially flag 300 suspects. P2 and P3 reduce this to 100 by removing low-quality and chemically irrelevant features. P4 identifies 20 linked to poor removal in a treatment plant. P5 finds 10 of these features in a toxic fraction, and P6 prioritizes five based on predicted risk. P7 can be used early to focus data analysis on informative chromatographic regions. This cumulative filtering, using a few strategies in combination, narrows down a complex dataset of features to a manageable number of compounds worth investigating further. It enables laboratories and regulators to focus on what matters: identification of high-risk CECs, assessment of exposure and hazard, and ultimately guiding environmental policy and remediation.

Conclusion

Prioritization is central to efficient NTS workflows. It prevents resources from being spent on uninformative signals and directs attention toward features most likely to represent relevant contaminants. This article has outlined seven complementary strategies that operate at different levels of the NTS process.

Combining approaches based on chemical structure, data quality, biological response, study design, and predictive modeling can accelerate identification and strengthen environmental risk assessment. The next step is to integrate these tools into reproducible, transparent, and scalable workflows, moving NTS from exploratory screening toward actionable regulatory support.

Non-Target Screening of Environmental Samples: Strategies for Quantification, Prioritization, and Identification Using LC-HRMS and Multidimensional Chromatography (KN46)

WE-07 – PFAS & Environmental

Wednesday, June 18, 14:15–16:00 pm

Reference

(1) Zweigle, J.; Tisler, S.; Bevilacqua, M.; et al. Prioritization Strategies for Non-Target Screening in Environmental Samples by Chromatography – High-Resolution Mass Spectrometry: A Tutorial. J. Chrom. A 2025, 1751, 465944. DOI: 10.1016/j.chroma.2025.465944

Jonathan Zweigle is a postdoctoral researcher at the University of Copenhagen focused on non-target screening approaches to identify emerging contaminants such as PFAS to understand their fate in the environment.

Selina Tisler is an assistant professor in the Analytical Chemistry group at the University of Copenhagen, specializing in advanced chemical analysis of micropollutants in water.

Marta Bevilacqua completed her PhD in chemometrics at the University of Rome “La Sapienza” in 2013 and has worked at the University of Copenhagen ever since. She now works as a data scientist in Jan H. Christensen’s Analytical Chemistry group.

Giorgio Tomasi is an associate professor in environmental chemometrics. He is responsible for chromatographic signal processing workflows at the Analytical Chemistry group at the University of Copenhagen.

Nikoline J. Nielsen is an associate professor in the Analytical Chemistry Group, Faculty of Science at UCPH. Her teaching and research evolve around chromatographic/mass spectrometric analysis of complex biological/environmental samples. She is currently heading the MSc-program in Environmental Science at UCPH.

Nadine Gawlitta is a postdoctoral researcher in the Analytical Chemistry group at the University of Copenhagen, Denmark. Her research focuses on the chemical characterization of environmental samples and the interaction of the environment and human health.

Josephine S. Lübeck is a postdoctoral researcher at the Faculty of Science, University of Copenhagen. She is specialized in analytical chemistry, with a focus on renewable biofuel analysis, supercritical fluid chromatography, and data analysis.

Age K. Smilde is affiliate professor at the Analytical Chemistry group, Faculty of Science of the University of Copenhagen. He is an expert in advanced multivariate data analysis.

Jan H. Christensen is a professor in environmental analytical chemistry. He is leader of the Analytical Chemistry group, Faculty of Science, University of Copenhagen, Denmark, and heads the Research Centre for Advanced Analytical