Determination of Basic Antidepressants and Their <i>N</i>-Desmethyl Metabolites in Raw Sewage and Wastewater Using Solid-Phase Extraction and Liquid Chromatography−Tandem Mass Spectrometry
Bibliographic record
Abstract
A novel analytical method has been developed for the determination of six basic antidepressants (venlafaxine, sertraline, paroxetine, citalopram, amitriptyline, and fluoxetine) and four of their metabolites (O-desmethylvenlafaxine, desmethylsertraline, nortriptyline, and norfluoxetine) in raw sewage and roughly primary-treated wastewater. For analytical development purposes, two ion exchange solid-phase extraction cartridges were compared. Extracts were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with positive-mode electrospray (+ESI) and selected reaction monitoring transitions. The choice of a basic mobile phase significantly improved the instrumental sensitivity (by up to 14-fold for norfluoxetine) relative to common +ESI acidic mobile phases. In addition to the remarkable gain in sensitivity, negligible matrix effects were also observed in the raw sewage samples. Analyte recoveries ranged from 80 to 103% and effluent detection limits from 0.048 to 0.10 ng/L. Samples collected at the Montreal Wastewater Treatment Plant showed the unequivocal presence of all the target compounds at concentrations of 2-346 ng/L. The target antidepressants were also detected in samples taken from the effluent receiving waters (i.e., the St. Lawrence River) but at lower concentrations (0.41-69 ng/L). The highly sensitive proposed method constitutes one of the best means for monitoring the environmental occurrence of tricyclic antidepressants, selective serotonin reuptake inhibitors (SSRIs), and some of their metabolites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".