Data supporting the optimization of liquid chromatography tandem mass spectrometry conditions to analyze EPA-priority hormones and bisphenol A in water samples
Bibliographic record
Abstract
This database presents the optimization of ultra-high-performance liquid chromatography electrospray ionization tandem mass spectrometry (UHPLC-MS/MS) for the analysis of EPA-priority endocrine disruptor compounds (13 hormones and bisphenol A). Various method parameters were tested and compared for improved sensitivity. Data related to the selection of the ionization source (heated-ESI vs. APCI) are presented, including optimization results of source parameters. Compound-dependent responses when varying the UHPLC mobile phase salt concentration of ammonium fluoride (NH4F) are supplied. Details on the chromatographic gradient program and chromatographic data demonstrating the separation of α-estradiol and β-estradiol are provided. In addition, we supply the details on mass spectrometry parameters under the optimized conditions, relative responses of quantification and confirmation MS/MS transitions (QT/CT), and number of points present in UHPLC-MS/MS spectra. The sample preparation and instrumental analysis procedures under the retained conditions are also described. The herein dataset supports the research “Analysis of Environmental Protection Agency priority endocrine disruptor hormones and bisphenol A in tap, surface and wastewater by online concentration liquid chromatography tandem mass spectrometry” Goeury et al., 2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".