Screening of seized drugs utilizing portable Raman spectroscopy and direct analysis in real time-mass spectrometry (DART-MS)

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.

Portable Digital Linear Ion Trap Mass Spectrometer Based on Separate-Region Corona Discharge Ionization Source for On-Site Rapid Detection of Illegal Drugs

As narcotic control has become worse in the past decade and the death toll of drug abuse hits a record high, there is an increasing demand for on-site rapid detection of illegal drugs. This work developed a portable digital linear ion trap mass spectrometer based on separate-region corona discharge ionization source to meet this need. A separate design of discharge and reaction regions was adopted with filter air as both carrier gas for the analyte and protection of the corona discharge needle. The linear ion trap was driven by a digital waveform with a low voltage (±100 V) to cover a mass range of 50–500 Da with a unit resolution at a scan rate of 10,000 Da/s. Eighteen representative drugs were analyzed, demonstrating excellent qualitative analysis capability. Tandem mass spectrometry (MS/MS) was also performed by ion isolation and collision-induced dissociation (CID) with air as a buffer gas. With cocaine as an example, over two orders of magnitude dynamic range and 10 pg of detection limit were achieved. A single analysis time of less than 10 s was obtained by comparing the information of characteristic ions and product ions with the built-in database. Analysis of a real-world sample further validated the feasibility of the instrument, with the results benchmarked by GC-MS. The developed system has powerful analytical capability without using consumables including solvent and inert gas, meeting the requirements of on-site rapid detection applications.

Spectral trends in GC-EI-MS data obtained from the SWGDRUG mass spectral library and literature: A resource for the identification of unknown compounds

Rapid identification of new or emerging psychoactive substances remains a critical challenge in forensic drug chemistry laboratories. Current analytical protocols are well-designed for confirmation of known substances yet struggle when new compounds are encountered. Many laboratories initially attempt to classify new compounds using gas chromatography-electron ionization-mass spectrometry (GC-EI-MS). Though there is a large body of research focused on the analysis of illicit substances with GC-EI-MS, there is little high-level discussion of mass spectral trends for different classes of drugs. This manuscript compiles literature information and performs simple exploratory analyses on evaluated GC-EI-MS data to investigate mass spectral trends for illicit substance classes. Additionally, this work offers other important aspects: brief discussions of how each class of drugs is used; illustrations of EI mass spectra with proposed structures of commonly observed ions; and summaries of mass spectral trends that can help an analyst classify new illicit compounds.