Comprehensive analysis by liquid chromatography Q‐Orbitrap mass spectrometry: Fast screening of peptides and organic molecules
Bibliographic record
Abstract
Abstract The number of substances nominally listed in the prohibited list of the World Anti‐Doping Agency increases each year. Moreover, many of these substances do not have a single analytical target and must be monitored through different metabolites, artifacts, degradation products, or biomarkers. A new analytical method was developed and validated for the simultaneous analysis of peptides and organic molecules using a single sample preparation and LC‐Q‐HRMS detection. The simultaneous analysis of 450 target molecules was performed after cleanup on a mixed‐mode solid‐phase extraction cartridge, combined with untreated urine. The cleanup solvent and reconstitution solvent were the most important parameters for achieving a comprehensive sample preparation approach. A fast chromatographic run based on a multistep gradient was optimized under different flows; the detection of all substances without isomeric coelution was achieved in 11 minutes, and the chromatographic resolution was considered a critical parameter, even in high‐resolution mass spectrometry detection. The mass spectrometer was set to operate by switching between positive and negative ionization mode for FULL‐MS, all‐ion fragmentation, and FULL‐MS/MS2. The suitable parameters for the curved linear trap (c‐trap) conditions were determined and found to be the most important factors for the development of the method. Only FULL‐MS/MS2 enables the detection of steroids and peptides at concentrations lower than the minimum required performance levels set by World Anti‐Doping Agency (1 ng mL−1). The combination of the maximum injection time of the ions into the c‐trap, multiplexing experiments, and loop count under optimized conditions enabled the method to be applied to over 10 000 samples in only 2 months during the 2016 Rio Summer Olympic and Paralympic Games. The procedure details all aspects, from sample preparation to mass spectrometry detection. FULL‐MS data acquisition is performed in positive and negative ion mode simultaneously and can be applied to untargeted approaches.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".