Comparison of APPI, APCI and ESI for the LC‐MS/MS analysis of bezafibrate, cyclophosphamide, enalapril, methotrexate and orlistat in municipal wastewater
Bibliographic record
Abstract
The applicability of three different ionization techniques: atmospheric pressure photoionization (APPI), atmospheric pressure chemical ionization (APCI) and electrospray ionization (ESI) was tested for the liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of five target pharmaceuticals (cyclophosphamide, methotrexate, bezafibrate, enalapril and orlistat) in wastewater samples. Performance was compared both by flow injection analysis (FIA) and on-column analysis in deionized water and wastewater samples. A column switching technique for the on-line extraction and analysis of water samples was used. For both FIA and on-column analysis, signal intensity and signal-to-noise (S/N) ratio of the target analytes in the three sources were studied. Limits of detection and matrix effects during the analysis of wastewater samples were also investigated. ESI generated significantly larger peak areas and higher S/N ratios than APCI and APPI in FIA and in on-column analysis. ESI was proved to be the most suitable ionization method as it enabled the detection of the five target compounds, whereas APCI and APPI ionized only four compounds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".