A targeted and non-targeted discovery screening approach for poly-and per-fluoroalkyl substances in model environmental biota samples
Bibliographic record
Abstract
A comprehensive analytical approach for targeted and non-targeted discovery screening of per- and polyfluoroalkyl substances (PFAS) was developed and applied to model complex environmental biotic samples. Samples were extracted by formic acid-acetonitrile solution and cleaned up and fractionated by SPE (WAX). Target PFAS quantification was performed by ultra-high performance liquid chromatography interfaced with a triple quadrupole mass spectrometer (UPLC-QqQ-MS/MS). Non-targeted analysis (NTA) PFAS screening was performed with UPLC coupled with a quadrupole-Exactive orbitrap high resolution mass spectrometer (UPLC-Q-Exactive-HRMS). An iterative exclusion (IE) approach was applied to data acquisition for NTA suspect screening to increase the potential for unknown PFAS discovery with MS/MS. A complex workflow in Compound Discoverer was set up to automate data processing of the PFAS suspects search. New mass lists and MS/MS databases, which included a large number of PFAS, were set up and introduced into the search for high-throughput structure identification using HRMS techniques. The integrated targeted-NTA method successfully analyzed for legacy and alternative PFAS in model environmental biota samples, namely polar bear liver and bird egg samples. Targeted analysis provided unequivocal identification of well known/established PFAS (mainly perfluoroalkyl acids) with quantification at very low levels. The NTA suspect screening was able to determine a broader range of PFAS. The data analysis method offered high-confidence annotations for PFAS despite lacking available authentic standards. Overall, the analytical coverage of PFAS was greater and elucidated other PFAS present in these model apex predators.
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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 | 0.001 | 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 | 0.001 |
| 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".