Chemometric strategies for the detection of bromazolam and xylazine in illicit opioids using surface-enhanced Raman and infrared spectroscopy
| dc.contributor.author | Martens, Rebecca Robinton | |
| dc.contributor.supervisor | Hore, Dennis Kumar | |
| dc.date.accessioned | 2024-08-27T21:29:26Z | |
| dc.date.available | 2024-08-27T21:29:26Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Chemistry | |
| dc.degree.level | Master of Science MSc | |
| dc.description.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. | |
| dc.description.embargo | 2025-08-22 | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.bibliographicCitation | Martens, R. R.; Gozdzialski, L.; Newman, E.; Gill, C.; Wallace, B.; Hore, D. K. "Trace Detection of Adulterants in Illicit Opioid Samples Using Surface-Enhanced Raman Scattering and Random Forest Classification." Anal. Chem. 96, 12277--12285 (2024). Copyright 2024 American Chemical Society. | |
| dc.identifier.uri | https://hdl.handle.net/1828/20318 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | machine learning | |
| dc.subject | illicit drug | |
| dc.subject | xylazine | |
| dc.subject | bromazolam | |
| dc.subject | SERS | |
| dc.subject | random forest | |
| dc.subject | opioid | |
| dc.title | Chemometric strategies for the detection of bromazolam and xylazine in illicit opioids using surface-enhanced Raman and infrared spectroscopy | |
| dc.type | Thesis |