This study develops a pattern recognition-integrated dual-channel colorimetric platform for on-site authentication of Chinese wolfberry (goji berry) geographical origins. The system combines two nanomaterial-based sensing mechanisms: (1) gold‑silver core-shell nanoparticles (Au@AgNPs) with chelation-dependent silver deposition, where region-specific Lycium barbarum polysaccharides (LBP) modulate Ag+-induced localized surface plasmon resonance shifts; (2) hollow FeNi Prussian blue nanocages (H-FeNi PBANCGs) that demonstrate peroxidase-mimetic activity, with antioxidant capacity variations altering 3,3′,5,5′-tetramethylbenzidine (TMB) oxidation kinetics. Regional LBP concentration differences trigger distinct dual-channel responses, with Qinghai LBP (93.44 μg/mL, 12 min aqueous extracts) strongly inhibiting Ag+ deposition and nanozyme-catalyzed TMB oxidation. Coupled with Linear Discriminant Analysis and Hierarchical Cluster Analysis, the platform achieves 94.44% classification accuracy for goji berries from five major Chinese provinces, with assay times (18 and 52 min) faster than instrumental analysis. This work integrates dual-channel sensing and pattern recognition for simple, accurate, field-deployable origin identification, supporting goji berry traceability and quality control practically.
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