Optimized machine learning approaches to combine surface-enhanced Raman scattering and infrared data for trace detection of xylazine in illicit opioids
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Issue Date
2025-01-17
Editor
Authors
Martens, Rebecca R.
Gozdzialski, Lea
Newman, Ella
Gill, Chris G.
Wallace, Bruce
Hore, Dennis K.
Subject
Abstract
Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies—hybrid (concatenated spectra), mid-level (extracted features from both datasets) and high-level (fusion of predictions from both models)—to enhance the predictive accuracy for xylazine detection in illicit opioid samples. Three 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 all fusion strategies. Validation results identified the random forest classifier as the optimal model for all fusion strategies, achieving high sensitivity (88% for hybrid, 92% for mid-level, and 96% for high-level) and specificity (88% for hybrid, mid-level, and high-level). The enhanced performance of the high-level fusion approach (F1 score of 92%) is demonstrated, effectively leveraging the surface-enhanced Raman data with a 90% voting weight, without compromising prediction accuracy (92%) when combined with infrared spectral data. This highlights the viability of a multi-instrument approach using data fusion and random forest classification to improve the detection of various components in complex opioid samples in a point-of-care setting.
Description
This article was originally published as: Marten, R.R., Gozdzialski, L, Newman, E., Gill, C., Wallace, B., & Hore, D.K. (2025). Optimized machine learning approaches to combine surface-enhanced Raman scattering and infrared data for trace detection of xylazine in illicit opioids. Analyst, 150(4), 700-711. https://doi.org/10.1039/d4an01496k