EXPRESS: Detection of Fentanyl Analogues in Illicit Drug Samples Using Surface-Enhanced Raman Spectroscopy and Random Forest Classification.

The ongoing overdose crisis is largely fueled by unregulated opioids; therefore, harm reduction measures like drug checking are forced to adapt to the changing supply. Even at low concentrations, fentanyl and its analogues can induce potent effects, particularly when present together. We found that the three most common fentanyl analogues occur individually and in mixtures of each other, and most often below 5% w/w, under the limit of consistent detection by Fourier transform infrared spectroscopy. Using surface-enhanced Raman scattering and random forest binary classifiers, we detected fentanyl (0.2-29.4% w/w), para-fluorofentanyl (0.2-20.0% w/w), and ortho-methyl fentanyl (0.8-27.4% w/w) in real-world samples with high accuracy (0.93, 0.85, 0.93) and precision (0.96, 0.90, 0.96), an improvement over Fourier transform infrared spectroscopy paired with random forest. However, while the surface-enhanced Raman scattering-based model performed exceptionally well for samples with only one target analyte, mixtures of analogues resulted in the suppression of signals from analytes at lower concentrations, complicating their identification. Overall, this demonstrates the need for the detection and distinction of fentanyl analogues below 5% w/w, the applicability of surface-enhanced Raman scattering and random forest classification for real-world samples involving one fentanyl analogue, and the need for future research in competitive binding of fentanyl analogues in mixtures.