Interconversion of ephedrine and pseudoephedrine during chemical derivatization
Bibliographic record
Abstract
Gas chromatography-mass spectrometry (GC-MS) analysis after heptafluorobutyric anhydride (HFBA) derivatization was one of the published methods used for the quantification of ephedrine (EP) and pseudoephedrine (PE) in urine. This method allows the clear separation of the derivatized diastereoisomers on a methyl-silicone-based column. Recently the authors came across a human urine sample with apparently high levels (µg/ml) of EP and PE upon initial screening. However, duplicate analyses of this sample using the HFBA-GC-MS method revealed an unusual discrepancy in the estimated levels of EP and PE, with the area response ratios of EP/PE at around 29% on one occasion and around 57% on another. The same sample was re-analyzed for EP and PE using other techniques, including GC-MS after trimethylsilylation and ultra-high-performance liquid chromatography-tandem mass spectrometry. Surprisingly, the concentration of EP in the sample was determined to be at least two orders of magnitude less than what was observed with the HFBA-GC-MS method. A thorough investigation was then conducted, and the results showed that both substances could interconvert during HFBA derivatization. Similar diastereoisomeric conversion was also observed using other fluorinated acylating agents (e.g. pentafluoropropionic anhydride and trifluoroacetic anhydride). The extent of interconversion was correlated with the degree of fluorination of the acylating agents, with HFBA giving the highest conversion. This conversion has never been reported before. A mechanism for the interconversion was proposed. These findings indicated that fluorinated acylating agents should not be used for the unequivocal identification or quantification of EP and PE as the results obtained can be erroneous.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".