An Integrated Molecular Networking Based Non-targeted PFAS Analysis Workflow by High-Resolution Mass Spectrometry (HRMS)

Polyfluoroalkyl Substances (PFAS) in the Environment
Poster Presentation

Presented by E. George
Prepared by J. Sanchez
Thermo Fisher Scientific, 55 River Oaks Pkwy, San Jose, CA, 95134, United States

Contact Information: [email protected]; 408-300-4267


PFAS are a pervasive group of enduring toxic chemicals that have attracted the attention of regulatory agencies globally. Conventional regulatory monitoring of PFAS has focused on developing targeted quantitative methods for a subset of offenders by LC-MS/MS. However, these methods are limited in scope due to the need for reference standards. Unlike traditional targets where reference standards are available, less than 200 PFAS standards exist for the more than 9,000 known PFAS, underscoring the need for a non-targeted workflow. Conventional non-targeted PFAS analysis workflows rely on direct spectral library matches, screening for signature fragments, homologous series with progressive retention times tied to chain length, negative mass defect (MD), and CF2 Kendrick MD. These conventional approaches are individually successful at identifying PFAS but aligning their outputs and exploiting them via integration with emerging techniques remains a challenge requiring expert knowledge in analytical chemistry and data science.

This presentation describes the comprehensive integration of emerging and conventional PFAS analysis techniques into a singular Compound Discoverer™ software workflow. Built-in data reduction approaches, including fragmentation-based target filtering leveraging similarity searches via the mzCloud™ spectral library, Fluoromatch Suite database containing over 700 PFAS signature fragments, and molecular networking using a CF2 transformation to connect homologous series, will be demonstrated. These approaches circumvent the lack of authentic standard availability, sparse coverage in spectral libraries, and limitations with negative mode in-silico fragmentation, enabling MS2 matching of PFAS absent from spectral libraries.

Additional data reduction tools, including extensive mass lists of known and theoretical PFAS, MD filtering thresholds specific to fluorine-containing compounds, CF2 Kendrick MD, and orthogonal MS1 PFAS discrimination plots, ensure the retention of only targets exhibiting PFAS characteristics. Finally, a molecular network generated from all the preserved targets is showcased by Perfluorosulfonic acid and Perflurosulfonamide clusters, encompassing homologous series constituents neglected by conventional techniques and unknowns.