Detection, discrimination and quantification of amphetamine, cathinone and <i>nor</i> ‐ephedrine regioisomers using benchtop <sup>1</sup> H and <sup>19</sup> F nuclear magnetic resonance spectroscopy
Bibliographic record
Abstract
Abstract Amphetamine and cathinone derivatives are abused recreationally due to the sense of euphoria they provide to the user. Methodologies for the rapid detection of the drug derivative present in a seized sample, or an indication of the drug class, are beneficial to law enforcement and healthcare providers. Identifying the drug class is prudent because derivatisation of these drugs, to produce regioisomers, for example, occurs frequently to circumvent global and local drug laws. Thus, newly encountered derivatives might not be present in a spectral library. Employment of benchtop nuclear magnetic resonance (NMR) could be used to provide rapid analysis of seized samples as well as identifying the class of drug present. Discrimination of individual amphetamine‐, methcathinone‐, N ‐ethylcathinone and nor ‐ephedrine‐derived fluorinated and methylated regioisomers is achieved herein using qualitative automated 1 H NMR analysis and compared to gas chromatography–mass spectrometry (GC–MS) data. Two seized drug samples, SS1 and SS2, were identified to contain 4‐fluoroamphetamine by 1 H NMR (match score median = 0.9933) and GC–MS (RR t = 5.42–5.43 min). The amount of 4‐fluoroamphetamine present was 42.8%–43.4% w/w and 48.7%–49.2% w/w for SS1 and SS2, respectively, from quantitative 19 F NMR analysis, which is in agreement with the amount determined by GC–MS (39.9%–41.4% w/w and 49.0%–49.3% w/w). The total time for the qualitative 1 H NMR and quantitative 19 F NMR analysis is ~10 min. This contrasts to ~40 min for the GC–MS method. The NMR method also benefits from minimal sample preparation. Thus, benchtop NMR affords rapid, and discriminatory, analysis of the drug present in a seized sample.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".