Development and Performance Evaluation of a Method for the Analysis of Per‐ and Polyfluoroalkyl Substances (PFAS) in Foods of Animal Origin
Bibliographic record
Abstract
Cases of PFAS contamination of foods have become more prominent in the media, causing a steep rise in concerns about potential health implications.To better understand dietary exposure and health risk, analytical methods for the analysis of a large variety of food products are required.This study focused on methods for PFAS extraction and analysis in complex food samples of animal origin.Increased sample preparation complexity is due to the presence of proteins and fats that can bind PFAS.For this study, an alkaline extraction was performed using sodium hydroxide in methanol, followed by solid phase extraction (SPE) clean-up using mixed mode Weak Anion Exchange (WAX) chemistry, with analysis performed using UHPLC-MS/MS.This extraction method was evaluated using a suite of 30 PFAS in six different food matrices: salmon, tilapia, ground beef, beef liver, beef kidney, and egg.Further, an interlaboratory study was performed to evaluate method performance for PFHxS, PFOS, PFOA, and PFNA quantitation in fish.Overall, detection and quantitation limits were determined to be in the sub-ng/g range for all matrices.Recoveries were within FDA criteria with utilization of isotope dilution for accurate correction of recovery during calculation of PFAS concentration in samples.Each of the seven participating labs successfully implemented the method and demonstrated acceptable accuracy, trueness, repeatability, and reproducibility.These studies confirm that the described method is suitable for compliance testing in accordance with EU regulations and for risk assessment purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".