Direct analysis in real time ‐ high resolution mass spectrometry (DART‐HRMS): a high throughput strategy for identification and quantification of anabolic steroid esters
Bibliographic record
Abstract
High throughput screening is essential for doping, forensic, and food safety laboratories. While hyphenated chromatography-mass spectrometry (MS) remains the approach of choice, recent ambient MS techniques, such as direct analysis in real time (DART), offer more rapid and more versatile strategies and thus gain in popularity. In this study, the potential of DART hyphenated with Orbitrap-MS for fast identification and quantification of 21 anabolic steroid esters has been evaluated. Direct analysis in high resolution scan mode allowed steroid esters screening by accurate mass measurement (Resolution = 60 000 and mass error < 3 ppm). Steroid esters identification was further supported by collision-induced dissociation (CID) experiments through the generation of two additional ions. Moreover, the use of labelled internal standards allowed quantitative data to be recovered based on isotopic dilution approach. Linearity (R(2) > 0.99), dynamic range (from 1 to 1000 ng mL(-1) ), bias (<10%), sensitivity (1 ng mL(-1) ), repeatability and reproducibility (RSD < 20%) were evaluated as similar to those obtained with hyphenated chromatography-mass spectrometry techniques. This innovative high throughput approach was successfully applied for the characterization of oily commercial preparations, and thus fits the needs of the competent authorities in the fight against forbidden or counterfeited substances.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".