Combining Pattern Analysis and Cyclic Ion Mobility Mass Spectrometry to Research Perand Poly Fluoroalkyl Substances (PFAS) Exposure in E-waste Handlers

Significance of the Topic

PFAS are persistent environmental contaminants of growing concern due to their toxicity and widespread use in industry, including electronics recycling. Combining cyclic ion mobility mass spectrometry with pattern analysis offers a comprehensive approach to detect both known and emerging PFAS in complex biological matrices such as human serum of e-waste workers.

Objectives and Study Overview

This study applied non-targeted UHPLC-cIM-MS with data independent acquisition to profile PFAS exposure in anonymized human serum from Ghanaian e-waste handlers. The goals were to identify known PFAS, uncover unknown species, and demonstrate the efficiency of a holistic pattern analysis workflow using dedicated software tools.

Methodology and Instrumentation

Anonymized serum samples were extracted via SPE mixed-mode plates. Separation employed UHPLC on an ACQUITY Premier system with C18 columns and PFAS kit modifications. Mass spectrometry used a SELECT SERIES Cyclic IMS in negative electrospray mode (m/z 50–1200) with ion mobility separation. Data acquisition and processing leveraged MassLynx and waters_connect with the Pattern Analysis Application to extract retention time, accurate mass, and collision cross section (CCS) data for all detected ions.

Instrumental Setup

• UHPLC: ACQUITY Premier System with PFAS kit and Atlantis BEH C18 AX column
• MS: SELECT SERIES Cyclic IMS for ion mobility and HDMSE acquisition
• Software: MassLynx 4.2, waters_connect 3.1, Pattern Analysis Application 1.0

Key Findings and Discussion

• Known PFAS (e.g., 6:2 FTS, PFHxS, PFOS isomers) were confirmed with ΔCCS <0.5% against library values, demonstrating high confidence in identification.
• Holistic pattern analysis using Kendrick mass defect plots, Kaufmann plots (m/C vs md/C), retention time vs m/z, and CCS trendlines enabled discrimination of PFAS features from thousands of matrix components.
• Putative assignment of a homologous series of unsaturated PFCAs not present in the library was achieved based on CCS fingerprints and elemental composition, highlighting the capacity to detect emerging PFAS without standards.

Benefits and Practical Applications

• Streamlined detection workflow for both known and unknown PFAS in complex samples
• Enhanced identification confidence through multi-criteria analysis (retention time, mass, CCS)
• Capability to monitor environmental exposure and discover emerging PFAS species for regulatory and health risk assessment

Future Trends and Applications

Advancements in machine learning for CCS prediction and expansion of high-resolution PFAS libraries will further improve non-targeted screening. Integration of real-time data processing and comprehensive pattern analysis will support large-scale environmental biomonitoring and faster risk evaluation for novel PFAS compounds.

Conclusion

The combination of UHPLC-cIM-MS and holistic pattern analysis using waters_connect software provides a robust platform for detailed PFAS profiling. This approach confidently identifies known compounds and uncovers unknowns, supporting comprehensive exposure assessment in high-complexity matrices.

References

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