A field-portable technology for illicit drug discrimination via deep learning of hybridized reflectance/fluorescence spectro-scopic fingerprints

Novel Psychoactive Substances (NPS) pose one of the greatest challenges across the illicit drug landscape. They can be highly potent, and coupled with rapid changes in structure, tracking and identifying these drugs is difficult, and presents users with a ‘Russian roulette’ if used. Benzodiazepines, synthetic opioids, synthetic cannabinoids and synthetic cathi-nones account for the majority of NPS related deaths and harm. Detecting these drugs with existing field-portable technol-ogies is challenging and has hampered the development of community harm reduction services and interventions. Herein, we demonstrate that hybridizing fluorescence and reflectance spectroscopies can accurately identify NPS and provide concentration information, with a focus on benzodiazepines and nitazenes. The discrimination is achieved through a deep learning algorithm trained on a library of pre-processed spectral data. We demonstrate the potential for these measure-ments to be made using a low-cost, portable device that requires minimal user training. Using this device, we demon-strate the discrimination of 11 benzodiazepines and from ‘street’ tablets that include bulking agents and other excipients. We show the detection of complex mixtures of multiple drugs, with the key example of nitazene + benzodiazepine (metonitazene + bromazolam), fentanyl + xylazine and heroin + nitazene (etonitazene) combinations. These samples represent current drug trends and associated with drug related deaths. When combined with the implementation of detection technology in a portable device, these data point to the immediate potential to support harm reduction work in community-based settings. Finally, we demonstrate that the approach may be more broadly generalized to other drug classes outside of NPS discrimination.