Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning.

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC-MS and LC-MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning-convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.

Comparison of portable and benchtop electrochemical instruments for detection of inorganic and organic gunshot residues in authentic shooter samples.

Analysis of gunshot residue currently lacks effective screening methods that can be implemented in real time at the crime scene. Historically, SEM-EDS has been the standard for analysis; however, advances in technology have brought portable instrumentation to the forefront of forensic science disciplines, including the screening of GSR. This study proposes electrochemical methods with disposable screen-printed carbon electrodes for GSR screening at the laboratory and points of care due to their rapid, cost-efficient, and compact platform. GSR residues were extracted from typical aluminum/carbon adhesive collection stubs and analyzed via square-wave anodic stripping voltammetry. Benchtop and portable electrochemical instruments were compared for the assessment and classification of authentic shooter samples by monitoring a panel of inorganic and organic GSR elements and compounds including lead, antimony, copper, 2,4-dinitrotoluene, diphenylamine, nitroglycerin, and ethyl centralite. The evaluation included the assessment of figures of merit and performance measures from quality controls, nonshooter, and shooter data sets. Samples collected from the hands of 200 background individuals (nonshooters), and shooters who fired leaded ammunition (100) and lead-free ammunition (50) were analyzed by the benchtop and portable systems with accuracies of 95.7% and 96.5%, respectively. The findings indicate that electrochemical methods are fast, sensitive, and specific for the identification of inorganic and organic gunshot residues. The portable potentiostat provided results comparable with the benchtop system, serving as a proof-of-concept to transition this methodology to crime scenes for a practical and inexpensive GSR screening that could reduce backlogs, improve investigative leads, and increase the impact of gunshot residues in forensic science.

The metabolism of valerylfentanyl using human liver microsomes and zebrafish larvae.

Valerylfentanyl, a novel synthetic opioid less potent than fentanyl, has been reported in biological samples, but there are limited studies on its pharmacokinetic properties. The goal of this study was to elucidate the metabolism of valerylfentanyl using an in vitro human liver microsome (HLM) model compared with an in vivo zebrafish model. Nineteen metabolites were detected with N-dealkylation-valeryl norfentanyl and hydroxylation as the major metabolic pathways. The major metabolites in HLMs were also detected in 30 day postfertilization zebrafish. An authentic liver specimen that tested positive for valerylfentanyl, among other opioids and stimulants, revealed the presence of a metabolite that shared transitions and retention time as the hydroxylated metabolite of valerylfentanyl but could not be confirmed without an authentic standard. 4-Anilino-N-phenethylpiperidine (4-ANPP), a common metabolite to other fentanyl analogs, was also detected. In this study, we elucidated the metabolic pathway of valerylfentanyl, confirmed two metabolites using standards, and demonstrated that the zebrafish model produced similar metabolites to the HLM model for opioids.

Evaluation of the Simultaneous Analysis of Organic and Inorganic Gunshot Residues Within a Large Population Data Set Using Electrochemical Sensors*, †.

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.