A comprehensive software workflow for non-targeted analysis of per- and polyfluoroalkyl substances (PFAS) by high-resolution mass spectrometry (HRMS)

Importance of the Topic

Per- and polyfluoroalkyl substances (PFAS) constitute a large class of persistent organic pollutants characterized by exceptional chemical stability and widespread environmental distribution. Traditional targeted LC-MS/MS approaches are constrained by the limited availability of certified reference materials and cannot address the thousands of known and emerging PFAS. High-resolution accurate mass spectrometry (HRAM) combined with non-targeted workflows is critical for comprehensive detection, characterization, and monitoring of PFAS across diverse matrices.

Objectives and Study Overview

This application note presents a unified untargeted PFAS analysis workflow implemented in Thermo Scientific™ Compound Discoverer™ software. It integrates multiple data-reduction and annotation strategies—drawn from established literature methods—to streamline the discovery and profiling of PFAS in complex environmental and biological samples.

Used Instrumentation

• Thermo Scientific™ Orbitrap Exploris™ 120 high-resolution mass spectrometer
• Thermo Scientific™ Vanquish™ Core Binary UHPLC system with PFAS Retrofit Kit
• Thermo Scientific™ Compound Discoverer™ software version 3.3 SP2
• mzCloud™ spectral library and FluoroMatch™ Suite database
• ChemSpider™ integration and a custom scripting node for orthogonal discrimination

Methodology and Workflow Details

Samples (e.g., water, municipal waste leachate, biological tissues) were acquired using full-scan MS1 and data-dependent MS2 in negative mode. Recommended experimental design includes at least one blank and three replicates per sample alongside pooled QCs and internal standards. The Compound Discoverer workflow comprises interconnected nodes for:

• Formula prediction (max 50 fluorine atoms)
• Spectral library searching (mzCloud) and signature ion matching (FluoroMatch Suite)
• Mass list searches against EPA DSSTox and theoretical PFAS lists
• Standard and CF2 Kendrick mass defect calculations
• ChemSpider chemical composition searches
• Background subtraction and peak quality filtering
• Custom scripting node for fragmentation-independent carbon estimation and orthogonal discrimination
• Molecular network generation for class-based PFAS clustering

A multistep filtering scheme—incorporating fragmentation-based filters, mass defect thresholds, formula constraints, and orthogonal QC filters—reduces feature lists from tens of thousands to fewer than 100 candidates.

Key Results and Discussion

In water samples spiked with 13 PFAS at EPA-relevant levels, the workflow achieved 91% sensitivity. Complex matrices initially yielded >14 000 features; sequential filters narrowed these to 28–60 meaningful PFAS candidates. Built-in visualizations, including Kendrick mass defect plots and molecular networks, successfully delineated homologous PFAS series and identified outliers (e.g., oxygen-substituted analogues). Retention time trends and MS2 match scores further validated putative identifications.

Benefits and Practical Applications of the Method

The integrated workflow provides a turnkey solution for non-targeted PFAS analysis, overcoming reference standard shortages via curated signature ion databases and in silico fragmentation. Onboard visualization tools expedite transition from raw data to actionable insights. Laboratories can confidently detect known and novel PFAS, supporting environmental monitoring, regulatory compliance, and research applications.

Future Trends and Opportunities

• Expansion of spectral libraries and compound class databases to cover emerging PFAS chemistries
• Integration of ion mobility separation for isomer resolution
• Application of machine learning algorithms for automated feature prioritization
• Real-time screening workflows and data interpretation automation
• Standardization and harmonization of non-targeted PFAS methods across laboratories
• Development of robust negative-mode in silico fragmentation models

Conclusion

The untargeted PFAS workflow in Compound Discoverer software 3.3 SP2 delivers an integrated, customizable platform for exhaustive PFAS profiling. By coupling advanced data reduction strategies with curated databases and extensive visualization tools, this approach enables laboratories to efficiently discover, annotate, and interpret PFAS contaminants, accelerating environmental risk assessment and regulatory decision-making.

References

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