Determination of Carbamazepine and Its Metabolites in Aqueous Samples Using Liquid Chromatography−Electrospray Tandem Mass Spectrometry
Bibliographic record
Abstract
A quantitative method is described for solid-phase extraction (SPE) followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the simultaneous analysis of carbamazepine and its five metabolites, 10,11-dihydro-10,11-epoxycarbamazepine, 10,11-dihydro-10,11-dihydroxycarbamazepine, 2-hydroxycarbamazepine, 3-hydroxycarbamazepine, and 10,11-dihydro-10-hydroxycarbamazepine. An SPE procedure was used to concentrate target compounds from aqueous samples collected from sewage treatment plant (STP) wastewater and surface water. Extracts were analyzed using electrospray LC-MS/MS with time-scheduled selected reaction monitoring. The recoveries of the analytes were 83.6-102.2% from untreated sewage (influent), 90.6-103.5% from treated sewage (effluent), and 95.7-102.9% from surface water samples. The instrumental detection limits were 0.8-4.8 pg for the analytes. Matrix effects were investigated for the analytes in HPLC-grade water, surface water, and STP influent and effluent. Ion suppression increased for analytes in order of surface water to STP effluent to STP influent, but no ion suppression was observed for analytes in HPLC-grade water. The developed method was validated by analysis of environmental aqueous samples: STP influent and effluent and surface water. Carbamazepine and all five metabolites were detected in STP influent and effluent samples. Only carbamazepine and 10,11-dihydro-10,11-dihydroxycarbamazepine were detected in the surface water sample. Notably, 10,11-dihydro-10,11-dihydroxycarbamazepine was detected at approximately 3 times higher concentrations than the parent drug, carbamazepine, in all of the aqueous samples. To our knowledge, this is the first report on the simultaneous determination of carbamazepine and its metabolites in environmental 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".