Explainable AI molecularly imprinted polymer electrochemiluminescence biosensor for discriminative methamphetamine...

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.