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Reliable identification of fentanyl and fentanyl analogs present in seized drug samples is imperative to the safety of first responders and laboratory personnel and informs the future analysis process and handling procedures. The electrochemical-surface enhanced Raman spectroscopy (EC-SERS) method developed in this work allows the in-situ preparation of the SERS substrate providing a rapid, efficient, and accurate approach to detect fentanyl, even at low percent by weight concentrations common in seized drugs. Optimization of the electrochemical potentials suitable for the SERS substrate preparation and adsorption of the analyte was achieved using multi-pulse amperometric detection. This method demonstrated large enhancement of the SERS response. This method was applied to six fentanyl analogs with substitutions to the amide group, representing small changes in the fentanyl core structure. Identification of these analogs through differences in the EC-SERS spectra was evident. Interference studies incorporating analytes frequently encountered with fentanyl including heroin, cocaine, methamphetamine, naltrexone, and naloxone were assessed and found to offer limited to no interference. The limits of detection of the fentanyl compounds were in the low to mid nanograms per milliliter range, with the most sensitive compound detected at 10 ng/ml. Application of the method to simulated drug mixtures was performed to determine fit-for-purpose. In all mixtures with fentanyl as the minor contributor, fentanyl was correctly identified, including mixture samples comprised of 5 and 1% fentanyl. This approach represents the first in-situ EC-SERS analysis of fentanyl and its analogs and provides accurate and efficient screening for fentanyl in seized drug samples.

Gunshot residue (GSR) refers to a conglomerate consisting of both organic molecules (OGSR) and inorganic species (IGSR). Historically, forensic examiners have focused only on identifying the IGSR particles by their...

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management.