Trace Detection of Adulterants in Illicit Opioid Samples Using Surface-Enhanced Raman Scattering and Random Forest Classification
Rebecca R. Martens, Lea Gozdzialski, E. J. Newman, Chris G. Gill, Bruce Wallace, Dennis K. Hore
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.