On-line solid phase extraction and liquid chromatography/tandem mass spectrometry to quantify pharmaceuticals, pesticides and some metabolites in wastewaters, drinking, and surface waters
Bibliographic record
Abstract
A simple on-line method was developed for the analysis of pharmaceuticals, pesticides and some metabolites in drinking, surface and wastewater samples. The technique is based on the use of on-line solid-phase extraction combined with liquid chromatography electrospray tandem mass spectrometry with positive electrospray ionization (LC-ESI(PI)-MS/MS). The injection of only 1 mL of filtered water sample is used with a total analysis time of 20 min, including the period required to flush the SPE cartridge with organic solvent and reconditioning the LC column. Method detection limits were in the range of 2 to 24 ng L(-1) for the compounds of interest, with recoveries from 87 to 110% in surface as well as wastewater samples. Matrix effects were observed for some compounds without exceeding more than 25%. All results displayed a good degree of reproducibility, with relative standard deviations (RSD) of less than 12% for all compounds. Moreover, at least 200 samples were analyzed without altering the performance of the pre-concentration column. This method was preferred over traditional off-line procedures because it minimizes tedious sample preparation, increases productivity and sample throughput. The analysis of various water and wastewater samples showed that caffeine, carbamazepine and atrazine could be detected in all the samples analysed and the selected compounds are always present in at least one of the sample types.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".