Improved identification of multiple drugs of abuse and relative metabolites in urine samples using liquid chromatography/triple quadrupole mass spectrometry coupled with a library search
Bibliographic record
Abstract
RATIONALE: Although two multiple reaction monitoring (MRM) transitions per compound are used for identification performed using liquid chromatography/triple quadrupole mass spectrometry (LC/QqQ-MS/MS), differences in identification criteria among several regulations may lead to misidentification. We demonstrated that the use of two MRM transitions and product ion spectra improves compound identification. METHODS: The scan cycle time was reduced using time-scheduled MRM (tMRM), data-dependent product ion scanning, and dynamic exclusion. The quantification and identification performance for 13 drugs of abuse and their metabolites were evaluated. RESULTS: Deuterated internal standards compensated for ion suppression. All analytes exhibited intra- and interday precision <12.11%, accuracy of -10.31% to +10.10%, and no carryover. The LC/QqQ-MS/MS and reference gas chromatography/MS methods were equally precise, accurate, and specific. Several regulatory organizations include two MRM transitions, their ratio, and retention time as identification criteria. In 28 samples, the relative ion ratio variation was >10% and product ion spectral matches with >94% probabilities improved drug and metabolite identification. CONCLUSIONS: The LC/QqQ-MS/MS method is a comprehensive assay in which tMRM and the product ion scan are combined in a single run by using a QqQ mass analyzer to simultaneously quantify amphetamine, ketamine, morphine, and their relative metabolites in urine. The proposed method can be applied in forensic science.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".