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.

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, ¹H 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.