Detection of Synthetic Opioids using AI-Enhanced Difference Raman Spectroscopy

The primary objective of Phase II effort is to advance the detection and classification of synthetic opioids, specifically unseen fentanyl analogs, using an approach that combines machine learning models with advanced fielded hardware. Building on the successes of Phase I, where we developed a robust framework for generating high-fidelity training data and created a Convolutional Neural Network model integrated with the Pendar X10 algorithm, Phase II will focus on expanding these capabilities to address the challenges of low-concentration mixtures and emerging synthetic opioids such as nitazenes. Key innovations include leveraging two advanced Pendar devices—the Pendar X10, a Tier 1 capability and an automated portable Raman microscope (Pendar XM) as a Tier 2 tool for detailed analysis. Our extensive spectral library of over 8,500 items, along with our established partnerships with state and local law enforcement agencies, allows us to collect diverse and representative datasets for robust testing and validation. Additionally, we will explore multi-class classification techniques, incorporating contextual knowledge of common cutting agents, to improve accuracy and reduce false positives. Our collaboration with the Global Forensics and Justice Center will facilitate pilot testing and field validation, ensuring that the developed solutions are ready for real-world deployment. Ultimately, we aim to integrate this AI-enhanced detection into the Pendar X10 and Pendar XM as a software update to provide law enforcement with reliable, field-ready tools for the rapid and accurate identification of synthetic opioids, improving public safety, and aiding in the ongoing fight against drug trafficking.