GRIFFINs and GoBleNs: A Non-Fictional Tool for Identifying Trace Controlled Substance Surrogates in Complex Mixtures

Abstract Drug overdose deaths have tripled over the past two decades, and methamphetamine remains the second most frequently identified drug involved in such fatalities. Within the United States, the Drug Enforcement Administration seizes tens of thousands of kg of clandestine powders, tablets, crystals, and liquids that may contain anywhere from 1−100% pure methamphetamine every year. Unfortunately, current portable techniques, such as Raman and infrared spectroscopy, fail to reliably identify methamphetamine and other analogs below ∼23 wt % due to interference from cutting agents. We introduce GRIFFINs, a tool for the gold-enhanced Raman identification for forensic field inspection of narcotics. GRIFFINs uses gold-blended nanoparticles (GoBleNs), a novel cationic, lipophilic-specific, and highly selective surface-enhanced Raman spectroscopic (SERS) substrate for the identification of low amounts (<23 wt %) of chemicals like alkylamines in complex matrices that mimic the components of real clandestine samples. Using machine learning, we trained the model on 10 independent dilution series for each of three controlled substance surrogates (phenylethylamine, serotonin, and dopamine) and three other SERS-responsive cutting agents (lidocaine, benzocaine, and caffeine) before testing at least 85 independent mixtures that mimic real evidence samples. The novel SERS substrate, GoBleNs, was mixed with HPLC-compatible samples before being analyzed with a commercially available, portable Raman spectrometer equipped with 830 nm excitation in less than 1 min. Using extreme gradient boosting discriminant analysis, limits of identification ranged between <0.1 and 0.1 wt % depending on the analyte. This study demonstrates the first application of SERS for improving the portable Raman analysis of trace amphetamine and tryptamine surrogates for seized drug analysis.