Balancing Selectivity and Cross-Sensitivity in Smart Tongue Sensing—Multimode, Multiplexed Optical Sensor Arrays for Dietary Supplement Classification

Achieving both universality and robustness in smart sensing remains a central yet unresolved challenge. Herein, we move beyond the conventional dichotomy of selective versus cross-sensitive sensors by demonstrating their synergistic integration within differential sensing architectures. Optical micro- and nanoparticle-based sensors—both selective (UW) and cross-sensitive (WUT)—were rationally combined to construct information-rich sensing arrays. Performance enhancement was explored considering three complementary strategies: the reduction of inter-sensor redundancy, the exploitation of full spectral fingerprints and multimode optical readout. Across independent datasets, log F analysis uncovered pronounced context-dependent chemosensitive particle behavior, enabling the identification of task-specific leading nanostructures while confirming metric robustness. Full-spectrum chemometric modeling delivered consistently high classification accuracy without overfitting, where SVM-DA excelled in resolving complex multiclass problems. Collectively, this work establishes a generalizable framework for engineering adaptive, high-dimensional sensing platforms, advancing the design of next-generation smart analytical systems, including smart noses and smart tongues.