Laser Diode Thermal Desorption/Atmospheric Pressure Chemical Ionization Tandem Mass Spectrometry Analysis of Selected Steroid Hormones in Wastewater: Method Optimization and Application
Bibliographic record
Abstract
A rapid and reliable method enabling high-throughput sample analysis for quicker data generation, detection, and monitoring of eight selected steroid hormones in water matrixes was developed and validated. Our approach is based on a novel sample introduction method, the laser diode thermal desorption/atmospheric pressure chemical ionization (LDTD/APCI) coupled to tandem mass spectrometry (MS/MS). The optimization of instrumental parameters and a method application are presented. Our method was successfully applied to spiked effluent wastewater in the low-nanogram per liter concentrations with total analysis time reduced to seconds (15 s) using LDTD/APCI-MS/MS compared to minutes with traditional liquid-chromatography coupled to tandem mass spectrometry (LC-MS/MS) following solid-phase extraction (SPE). The instrumental detection limits for LDTD/APCI-MS/MS ranged from 5 to 24 microg L(-1) and from 13 to 43 ng L(-1) for the method detection limits. Calibration curves in wastewater matrix showed good linearity (R(2) > 0.99), and precision (intraday and interday) was below 20%. This work demonstrates that LDTD/APCI-MS/MS could be used for fast and effective quantitative analysis of emerging contaminants in different water matrixes with reduced cost by eliminating the chromatography step used in traditional LC-MS/MS.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".