Per- and polyfluoroalkyl substances (PFAS) are turning out to be far more numerous, widespread, and insidious than once believed. While targeted testing tracks only a few dozen compounds, new non-targeted techniques such as SWATH DIA are uncovering many more – with direct implications for food safety, environmental monitoring, and regulation.
We spoke with Craig Butt, Senior Manager of Applied Markets, Scientific Marketing at SCIEX – who has spent more than two decades in the PFAS field – about the evolving challenges of PFAS analysis and the promise of mass spectrometry in unmasking hidden risks.
How long has PFAS been on your radar and why did you become interested in this area?
I first learned about PFAS as a Master’s student and I became so fascinated that I decided to focus my PhD research at the University of Toronto on understanding how PFAS accumulated in arctic animals with no documented exposure. Fast forward to post-doctoral research, I was a NSERC post-doctoral research fellow in the Nicholas School of the Environment at Duke University, later becoming a research scientist in the department. My research at Duke shifted slightly to understanding the in vitro toxicology of chemical flame retardants, as well as human exposure levels of these chemicals alongside PFAS.
Since then, I have been involved in developing new and exciting methods analyzing PFAS and persistent organic contaminants using mass spectrometry. Over the past 20 years, my interest in PFAS has grown because the research and its implications are constantly evolving. Our own exposure, PFAS levels in the environment, and ultimately the human health effects are all issues we will have to deal with in our lifetime.
What are some of the big challenges in PFAS analysis today?
Reviewing and processing non-targeted data is difficult and very time consuming. The data files are large and discovering novel PFAS compounds is typically a laborious, manual process. Further, determining the chemical structures of these unknown PFAS is challenging.
However, this is an area where AI and machine learning may be able to help. For example, Si Wei and his team at Nanjing University's School of Environment in China developed a machine learning platform to automate these processes. In a 2023 study, their platform ultimately led to the discovery of 17 new PFAS in the wastewater samples. This is important because it improves our understanding of the PFAS burden released into the environment. Presently targeted PFAS testing methods only monitor ~30-40 compounds.
What wider implications do these findings have?
These findings demonstrate that we are likely exposed to a wider number of PFAS than originally thought. In the example above, Wei and his team discovered the novel PFAS in wastewater from a fluorochemical plant. If untreated, this wastewater may be emitted into surface waters and potentially make its way into drinking water. In addition, the PFAS in the surface water may bioaccumulate into fish or be used to irrigate agricultural crops – making their way into our food.
Si Wei’s team used SWATH DIA in their study. Could you explain how the SWATH DIA method works and how it could improve the detection of PFAS?
SWATH DIA (data independent acquisition) is a type of acquisition technique that collects MS/MS fragmentation spectra for all chemicals in a sample. This is important because the MS/MS spectrum provides a molecular fingerprint that is used to either confirm known PFAS structures or identify novel PFAS structures. For example, researchers can compare the acquired MS/MS spectrum to a library database to confirm the PFAS compound identity. Alternatively, the structure of novel PFAS compounds can be elucidated by understanding the individual fragments in the MS/MS spectrum and piecing them together. The MS/MS spectra is key to determining the exact structure of the PFAS chemical.
Researchers can use SWATH DIA to ensure they fully understand the complete PFAS burden in a food sample, for example. Targeted techniques only monitor for a relatively small number of PFAS compounds. SWATH DIA allows researchers to screen for chemicals that they weren’t initially looking for, or didn’t know to look for, also known as unknown screening.
Looking ahead, what do you see as the next steps in PFAS research and its regulation, particularly in relation to safeguarding food safety?
In the future, I think that non-targeted acquisition techniques, such as SWATH DIA, will be increasingly used as routine screening for food safety. Software will become more streamlined, and data processing will become faster, more efficient and even more powerful. MS/MS spectral libraries will become larger, allowing researchers to more confidently identify the full range of PFAS in their samples. Further, I think that researchers will continue to look “upstream” and investigate the root cause of food contamination. This includes looking at the soil that crops are grown in, the water that is fed to livestock, and the food packaging that processed foods are shipped and stored in.
Have there been any recent PFAS research using SWATH DIA that has caught your eye?
I recently came across an exciting preprint from the University of Vienna that demonstrates a proof-of-concept "exposomics" method for screening contaminants in human blood using a combined SWATH DIA and MRMHR acquisition on the ZenoTOF 7600 system.
“Exposomics” attempts to understand the impacts of our exposure to all chemicals in the natural and built environment. As such, it requires very broad analytical techniques such as SWATH DIA. In addition, the analytical methods must be sensitive since contaminant levels in humans (e.g., blood, urine) are typically very low. The researchers presented a proof-of-concept method, which combined a non-targeted SWATH DIA with a targeted MRMHR acquisition in a single run, resulting in broad analyte coverage and ultra-trace level sensitivity.
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