Trace Detection of Adulterants in Illicit Opioid Samples Using Surface-Enhanced Raman Scattering and Random Forest Classification.

Abstract

The detection of trace adulterants in opioid samples is an important aspect of drug checking, a harm reduction measure that is required as a result of the variability and unpredictability of the illicit drug supply. While many analytical methods are suitable for such analysis, community-based approaches require techniques that are amenable to point-of-care applications with minimal sample preparation and automated analysis. We demonstrate that surface-enhanced Raman spectroscopy (SERS), combined with a random forest classifier, is able to detect the presence of two common sedatives, bromazolam (0.32-36% w/w) and xylazine (0.15-15% w/w), found in street opioid samples collected as a part of a community drug checking service. The Raman predictions, benchmarked against mass spectrometry results, exhibited high specificity (88% for bromazolam, 96% for xylazine) and sensitivity (88% for bromazolam, 92% for xylazine) for the compounds of interest. We additionally provide evidence that this exceeds the performance of a more conventional approach using infrared spectral data acquired on the same samples. This demonstrates the feasibility of SERS for point-of-care analysis of challenging multicomponent samples containing trace adulterants.