Neural Network-Based Detection of Adulterants in Opioid Samples Using IR Absorption Spectroscopy

Community-based drug checking services are challenged in their ability to reliably detect low concentration adulterants that are increasingly present in the illicit drug supply. Spectral signatures from commonly used field instruments such as infrared spectrometers require careful analysis to identify characteristic features in a complex mixture. In this study, we train neural network models for the detection of bromazolam and para-fluorofentanyl, using infrared absorption data collected at a point-of-care drug checking service. The neural network models classified the two components with an F1-score of 0.88 for bromazolam and 0.89 for para-fluorofentanyl. In comparison, a random forest model optimized using the same data set had an F1-score of 0.66 for bromazolam and 0.76 for para-fluorofentanyl. This demonstrates that neutral networks are excellent candidates for such complex drug detection applications and outperform other machine learning-based approaches.