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Topic

Chemometric Strategies for the Detection of Bromazolam and Xylazine in Illicit Opioids Using Surface-Enhanced Raman and Infrared Spectroscopy

Department of Chemistry

Date & location

• Thursday, August 22, 2024
• 10:30 A.M.
• Elliott Building, Room 228

Examining Committee

Supervisory Committee

• Dr. Dennis Hore, Department of Chemistry, ·¬ÇÑÉçÇø (Supervisor)
• Dr. Alexandre Brolo, Department of Chemistry, UVic (Member)

External Examiner

• Dr. Li-Lin Tay, Measurement Science and Standards, NRC Canada

Chair of Oral Examination

• Dr. Tobias Junginger, Department of Physics and Astronomy, UVic

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, 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 for the compounds of interest (88% for bromazolam, 96% for xylazine) and sensitivity (88% for bromazolam, 92% for xylazine). 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 surface-enhanced Raman spectroscopy for point-of-care analysis of challenging multi-component samples containing trace adulterants.

Surface-enhanced Raman spectroscopy and infrared spectroscopy were integrated into two data fusion strategies - hybrid (concatenated spectra) and high level (fusion of high outputs from both models) - to enhance the predictive accuracy for xylazine detection. Three advanced chemometric approaches - random forest, support vector machine, and k-nearest neighbor algorithms - were employed and optimized using a 5-fold cross-validation grid search for both fusion strategies. Validation results identified the random forest classifier as the optimal model for both fusion strategies, achieving high sensitivity (88% for hybrid, 84% for high level) and specificity (88% for hybrid, 92% for high level). We demonstrate the enhanced practicality of the high level fusion approach, effectively leveraging the surface-enhanced Raman data with a 90% voting weight, without compromising prediction accuracy when combined with infrared spectral data. This highlights the viability of a multi-instrumental approach using data fusion and random forest classification to improve the detection of various components in complex opioid samples for community-based drug checking.