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Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
Published on: September 16, 2014
Pattern recognition-based dual-channel colorimetric platform for on-site and accurate identification of Chinese
Dai Lu1, Chen Xie1, Jialing Liu1
1TCM and Ethnomedicine Innovation & Development International Laboratory, School of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, PR China.
None:
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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