Comparative study of the analysis of seized samples by GC-MS, 1H NMR and FT-IR spectroscopy within a Night-Time Economy (NTE) setting
Bibliographic record
Abstract
Rapid analysis of surrendered or seized drug samples provides important intelligence for health (e.g. treatment or harm reduction), and custodial services. Herein, three in-situ techniques, GC-MS, 1H NMR and FT-IR spectroscopy, with searchable libraries, are used to analyse 318 samples qualitatively, using technique specific library-based searches, obtained over the period 24th – 29th August 2019. 259 samples were identified as consisting of a single component, of which cocaine was the most prevalent (n = 158). Median match scores for all three techniques were ≥ 0.84 and showed agreement except for metformin (n = 1), oxandrolone (identified as vitamin K by IR (n = 4)), diazepam (identified as zolpidem by FT-IR (n = 2)) and 2-Br-4,5-DMPEA (n = 1), a structural isomer of 2C-B identified as a polymer of cellulose (cardboard) by FT-IR. 51 samples were found to consist of two or more components, of which 49 were adulterated cocaine samples (45 binary and 4 tertiary samples). GC-MS identified all components present in the 49 adulterated cocaine samples, whereas IR identified only cocaine in 88 % of cases (adulterant only = 12 %). The breakdown for 1H NMR spectroscopy was all components identified (51 %), cocaine only (33 %), adulterant only (10 %), cocaine and one adulterant (tertiary mixtures only, 6 %).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".