Fentanyl detection and quantification using portable infrared absorption spectroscopy
Bibliographic record
Abstract
Community drug checking is a harm reduction strategy that is currently being employed \nin response to the ongoing overdose crisis in North America. The Vancouver Island Drug \nChecking Project uses a variety of methods to analyze drug samples including fentanyl \nand benzodiazepine immunoassay test strips, attenuated total reflection Fourier transform \ninfrared spectroscopy, Raman spectroscopy and gas chromatography–mass spectrometry. \nA study was designed to examine the combined ability of infrared spectroscopy and partial \nleast squares regression to quantify the fentanyl content of illicit opioids. Binary and \nternary mixtures of powdered fentanyl HCl, anhydrous caffeine and sugar alcohols were \nprepared as standards representative of the opioids samples presented for drug checking. \nThe infrared spectra of each set of standards was used to train and test individual partial \nleast squares regression models. A grid search was employed to optimize the number of \nlatent variables and data pre-processing strategy for each model. A robust partial least \nsquares regression model, trained on all four sets of standards, was shown to accurately \nquantify fentanyl content. This model was then used to evaluate the fentanyl concentration \nof samples brought in for drug checking. In the period October 2018 to December 2020, \nthe detected level of fentanyl in opioid samples had a mean concentration of 10% with a \nstandard deviation of 7%. \nIllicit opioids which contained caffeine hydrate were discovered upon examination of \nthe anomalous service samples that were identified by local outlier factor. The infrared \nspectrum of caffeine shows subtle changes upon its hydration, which are due to differences in the hydrogen bonding pattern of the two forms of caffeine. Standards containing semi-hydrated caffeine and fentanyl HCl were prepared and analyzed to explore the effect of caffeine hydration on fentanyl quantification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".