Determination of pharmaceuticals in aqueous samples using positive and negative voltage switching microbore liquid chromatography/electrospray ionization tandem mass spectrometry
Bibliographic record
Abstract
Analytical methods were developed for atorvastatin, novobiocin and roxithromycin using microbore liquid chromatography/electrospray ionization tandem mass spectrometry (microbore LC/ESI-MS/MS) in positive and negative voltage switching mode. Atorvastatin and roxithromycin require the positive-ion mode, whereas the negative-ion mode is required for the determination of novobiocin. Using the positive and negative voltage switching function, the three analytes were determined with one injection, and the time required was half that required using separately run positive- and negative-ion modes, without any reduction in sensitivity. A microbore LC column (100 x 1.0 mm i.d.) was chosen for chromatographic separation with mobile phase solvents acetonitrile and 10 mM aqueous ammonium acetate. The flow-rate was 0.1 ml min(-1) and the injection volume was 1 micro l. The analytes were quantified in the multiple reaction monitoring mode with external standards. By switching the positive and negative voltage, the three analytes were determined with a 4 min chromatographic run and with instrumental detection limits of 1-3 pg. This analytical method, using a microbore LC column combined with solid-phase extraction, was applied successfully to the determination of trace levels of the above pharmaceuticals in aqueous samples. Atorvastatin was detected in a sewage treatment plant final effluent.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".