Determination of bezafibrate, methotrexate, cyclophosphamide, orlistat and enalapril in waste and surface waters using on-line solid-phase extraction liquid chromatography coupled to polarity-switching electrospray tandem mass spectrometry
Bibliographic record
Abstract
We developed a rapid method for the monitoring of five selected pharmaceuticals in the influent and effluent of municipal wastewater treatment plants (WWTP) as well as in the effluent-receiving waters. To that end, we optimized and validated an analytical method based on on-line solid-phase extraction (on-line SPE) coupled with reversed-phase liquid chromatography-switching polarity electrospray ionization-tandem mass spectrometry (LC-ESI(+/-)-MS/MS). The target analytes have a variable hydrophobic character and belong to various therapeutic classes including the lipid regulator bezafibrate, the chemotherapy drugs methotrexate and cyclophosphamide, the lipase inhibitor orlistat and the angiotensin converting enzyme (ACE) inhibitor used in the treatment of hypertension, enalapril. The method combines positive and negative voltage switching modes, therefore all analytes can be determined using a single injection and without any reduction in sensitivity. In order to detect traces of these compounds, a preconcentration step before detection is performed by loading 1.00 mL of sample in an on-line SPE cartridge and eluting from the cartridge using a reversed-phase liquid chromatography gradient. Analysis of wastewater and surface water samples was greatly affected by co-eluting matrix compounds, to compensate for matrix effects quantitation was therefore performed using standard additions. Method intra-day precision was less than 6.5% and limits of detection in fortified matrix effluent samples ranged from 9 to 20 ng L(-1). Four of the target pharmaceuticals were detected in the WWTP effluents, enalapril and bezafibrate being the most abundant compounds with concentrations of 35 and 239 ng L(-1), respectively. Concentrations of these same compounds in surface water samples from sites downstream in the St. Lawrence River were 8 and 63 ng L(-1), respectively, which was mainly due to dilution.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".