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 in response to the ongoing overdose crisis in North America. The Vancouver Island Drug Checking Project uses a variety of methods to analyze drug samples including fentanyl and benzodiazepine immunoassay test strips, attenuated total reflection Fourier transform infrared spectroscopy, Raman spectroscopy and gas chromatography–mass spectrometry. A study was designed to examine the combined ability of infrared spectroscopy and partial least squares regression to quantify the fentanyl content of illicit opioids. Binary and ternary mixtures of powdered fentanyl HCl, anhydrous caffeine and sugar alcohols were prepared as standards representative of the opioids samples presented for drug checking. The infrared spectra of each set of standards was used to train and test individual partial least squares regression models. A grid search was employed to optimize the number of latent variables and data pre-processing strategy for each model. A robust partial least squares regression model, trained on all four sets of standards, was shown to accurately quantify fentanyl content. This model was then used to evaluate the fentanyl concentration of samples brought in for drug checking. In the period October 2018 to December 2020, the detected level of fentanyl in opioid samples had a mean concentration of 10% with a standard deviation of 7%. Illicit opioids which contained caffeine hydrate were discovered upon examination of the anomalous service samples that were identified by local outlier factor. The infrared spectrum 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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".