Determination of organophosphate flame retardants and plasticizers in lipid-rich matrices using dispersive solid-phase extraction as a sample cleanup step and ultra-high performance liquid chromatography with atmospheric pressure chemical ionization mass spectrometry
Bibliographic record
Abstract
A fast, robust and highly sensitive analysis method for determination of trace levels of organophosphate ester (OPE) flame retardants and plasticizers in lipid-rich samples was presently developed, and based on ultra-high performance liquid chromatography-tandem mass spectrometry coupled to a positive atmospheric pressure chemical ionization source (UHPLC-MS/MS-APCI(+)). The target OPEs in the sample were extracted from the biota samples, such as egg and liver, by ultrasonic extraction, and cleaned up further by dispersive solid phase extraction (d-ESP). As a result, background contamination was largely reduced. Different dispersive ESP sorbents were tested and primary secondary amine (PSA) bonded silica sorbents showed the best recoveries for these target OPEs. The recoveries obtained were in the range 54-113% (RSD<17%), with method limits of quantification (MLOQs) ranging between 0.06 and 0.29ng/g in egg, and 0.05 and 0.50ng/g w.w. in liver sample. The matrix effects (MEs) associated with using APCI(+) and ESI(+) sources were investigated. APCI(+) showed much less ion suppression than ESI(+) for the determination of these OPEs. For egg and liver samples, the APCI(+) ME values ranged from 40% to 94%, while ESI(+) ME values ranged from 0% to 36%. Although APCI(+) was used for the determination of OPEs, the ionization mechanism might mainly be a thermospray ionization process. This UHPLC-MS/MS-APCI(+) method showed good response linearity for calibration (R2>0.99). The proposed method was applied to real environmental bird egg and fish samples, where several OPE were quantifiable and different OPE patterns was observed between 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".