Solid-phase microextraction with recessed matrix compatible coating for in situ sampling of per- and polyfluoroalkyl substances in meat
Bibliographic record
Abstract
This study presents a novel method for in situ extraction of per - and polyfluoroalkyl substances (PFAS) from intact meat samples using a recessed solid phase microextraction (SPME) device coupled with LC-MS/MS. The SPME device with matrix-compatible coating (HLB-WAX/PAN) in the recessed section, exhibited mechanically robust and low matrix effects in meat samples (−13.7–11.1 %). Key parameters influencing extraction efficiency, including extraction time, adsorbent amount, extraction temperature, and desorption time were comprehensively optimized. The stability of PFAS adsorbed onto the coating during storage at different temperatures and durations was also assessed. Under optimized conditions, the proposed method demonstrated applicability across pork, beef, and lamb tissues with excellent linearity (R 2 ≥ 99.32 %), good sensitivity (LOD in the range of 0.01–1.52 ng/g), as well as acceptable accuracy and reproducibility (intra-day and inter-day). Compared with conventional methods, the SPME-LC-MS/MS method shows the advantages of simple operation, short extraction time and low organic solvent consumption with low matrix effects. This approach offers a straightforward and reliable solution for direct in situ monitoring PFAS in commercial meat samples and has potential for on-site application. • Recessed SPME device with matrix-compatible coating (HLB-WAX/PAN) is designed. • The device is used as on-site sampler for in-situ extraction of PFAS in intact meat. • The work offers a simple and reliable solution for monitoring PFAS in meat samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".