Explainable AI molecularly imprinted polymer electrochemiluminescence biosensor for discriminative methamphetamine and amphetamine detection

Abstract

Summary Independent forensic quantification of methamphetamine (MA) and amphetamine (AMP) remains challenging due to their near-identical structures and overlapping electrochemical signatures. We report an end-to-end AI-integrated MIP-ECL biosensing framework employing target-specific MIP-NP/MWCNT/Nafion/Ir(pq)2(acac)/SPCE platforms. The Ir(III) luminophore within an MWCNT/Nafion matrix produced markedly superior ECL emission over the [Ru(bpy)3]2+ benchmark (40 mM DBAE, pH 8.0). MIP nanoparticles conferred high selectivity against structurally related interferents, validated in saliva, urine, and blood plasma, achieving detection limits of 0.11 ng mL−1 for MA and 0.28 ng mL−1 for AMP. A structured explainable machine learning pipeline employing CatBoost with Group-K-Fold cross-validation eliminated data leakage and achieved test R2 values of 0.9864 (MA) and 0.9675 (AMP). SHAP analysis confirmed that model predictions were consistent with known electrochemical kinetics. This modular framework establishes a generalizable paradigm for intelligent MIP-ECL biosensing with direct applicability in forensic toxicology and point-of-care diagnostics.