High throughput analysis of solid-bound endocrine disruptors by LDTD-APCI-MS/MS
Bibliographic record
Abstract
The development of a high-throughput method for the analysis of 14 endocrine-disrupting substances in environmental solid matrices has been investigated. Selected compounds were: hormones (estrogens and progestogens), parabens and triclocarban. The ultrafast method (15 s per sample) is based on the laser diode thermal desorption-atmospheric pressure chemical ionization (LDTD-APCI) coupled to tandem mass spectrometry (MS/MS). This novel approach was tested and validated in three different solid matrices (municipal sludge cakes, aquatic sediments and agricultural soils) and its performance was evaluated by estimation of extraction recovery, linearity, precision, and detection limits. In contrast to other methods based on LC-MS/MS, a cleanup step is not necessary or minimal for the municipal sludge cake matrix. Extraction recoveries ranged from 80 to 109% for all compounds in all matrix types except for estriol which was 60-75%. The intra- and inter-day precisions, as indicated by % RSD, were ≤ 14% and ≤ 16%, respectively. The method detection limits ranged from 0.7 to 4.0 ng g⁻¹ in sediments and soil matrices and 2.8 to 16.8 ng g⁻¹ for municipal sludge cake samples. The results for real environmental samples collected in different areas of Quebec (Canada) are illustrated.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".