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Related Experiment Video

Updated: Feb 19, 2026

Author Spotlight: An Efficient Methodology to Confidently Differentiate and Characterize Fentanyl Analogs
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Development and validation of a fentanyl quantification model for drug checking services.

Samuel Tobias1,2, Sara M Guzman3, Jason E Hein3

  • 1British Columbia Centre on Substance Use, 400-1190 Hornby Street, Vancouver, BC V6Z 2K5, Canada.

Drug and Alcohol Dependence Reports
|February 18, 2026
PubMed
Summary

This study developed a machine learning model to accurately quantify fentanyl and fluorofentanyl in unregulated drug samples. This advanced drug checking technology improves harm reduction by providing more precise fentanyl concentration estimates.

Keywords:
Drug checkingFentanylHarm reductionMachine learning

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Area of Science:

  • Forensic Chemistry
  • Data Science
  • Public Health

Background:

  • Community drug checking services are vital for harm reduction but face challenges in accurately quantifying fentanyl concentrations.
  • The unregulated opioid supply exhibits significant variability in fentanyl content, complicating analysis.
  • Existing drug checking technologies require enhancement for precise fentanyl analogue quantification.

Purpose of the Study:

  • To develop the first machine learning model capable of classifying and quantifying multiple fentanyl analogues in community drug checking samples.
  • To improve the accuracy of fentanyl concentration measurements in unregulated drug supplies.
  • To integrate advanced analytical techniques with community-based harm reduction tools.

Main Methods:

  • Fourier-transform infrared spectroscopy (FTIR) was used for initial drug sample analysis at harm reduction sites.
  • Laboratory analysis at Health Canada's Drug Analysis Service provided gold-standard quantitative nuclear magnetic resonance (qNMR) results.
  • A machine learning pipeline, including ridge regression and random forest models, was developed and optimized for fentanyl and fluorofentanyl quantification.

Main Results:

  • A hybrid machine learning pipeline demonstrated high accuracy in estimating fentanyl (mean average error=3.74; R²=0.94) and fluorofentanyl (mean average error=1.22; R²=0.97) concentrations.
  • Model performance varied based on sample composition, with different algorithms excelling in pure fentanyl versus mixed-analogue samples.
  • Over a six-year period, 131,096 drug checks were conducted, with 2032 unique samples analyzed, providing a robust dataset for model training.

Conclusions:

  • Machine learning models can significantly augment traditional community drug checking technologies for fentanyl quantification.
  • Accurate quantification of fentanyl analogues is crucial for effective harm reduction strategies.
  • Communicating prediction uncertainty is essential for the responsible implementation of these advanced tools in point-of-care settings.