The INTREPID program: Linking illicit fentanyl‐containing tablets and powders seized at US borders to a common origin by combining physical/chemical identifiers and machine learning

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Abstract Three US government agencies, the Drug Enforcement Administration (DEA), Customs and Border Protection (CBP), and Food and Drug Administration (FDA), established a collaborative program to leverage each agency's expertise and capabilities to characterize illicit fentanyl‐containing tablets and powders collected at US ports of entry (POEs) for attribution and sourcing purposes. The Intelligence National Threat Response–El Paso Illicit Drug (INTREPID) program employed a proof‐of‐concept pilot study that combined physical and chemical analytical techniques with machine learning to establish comprehensive sample profiles. Samples were analyzed using complementary techniques including color imaging, three‐dimensional surface analysis, and multi‐technique chemical profiling (Raman, FT‐IR, GC–MS, LC–MS, ICP‐MS, 1 H NMR). Machine learning models were employed solely as screening tools to prioritize samples for detailed human analysis; all conclusions were based on direct analytical comparison, not on machine learning results. Physical analyses plausibly linked fentanyl tablet seizures from two different ports of entry collected 8 weeks apart based on consistent color characteristics and three‐dimensional debossing patterns. Chemical analyses plausibly linked fentanyl powder seizures from the same port of entry collected 3 h apart with identical qualitative profiles and minimal quantitative variation (16.7% concentration error). Although alternative explanations for the similarities observed between these sample pairs cannot be excluded, these findings demonstrate the feasibility of using combined physical and chemical data within a forensic intelligence framework to generate actionable leads for law enforcement investigations of illicit fentanyl trafficking networks.

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