Biosensors have achieved remarkable advances in sensitivity and miniaturization, yet their translation from laboratory prototypes to robust real-world devices remains constrained by drift, matrix effects, cross-sensitivity, and limited multiplexing capability. A fundamental bottleneck lies in the univariate interpretation of intrinsically multidimensional signals, which discards structured information embedded in electrochemical, optical, and spectroscopic responses. In this perspective, we argue that chemometrics is not a post-processing accessory but an enabling architectural component of next-generation biosensors. By modeling full signal profiles rather than isolated features, chemometric strategies can broaden selectivity through informational discrimination, compensate structured drift, enable multiplexing without proportional increases in chemical complexity, and push detection limits beyond apparent hardware constraints. Across representative case studies spanning electrochemical, optical, and hybrid platforms, we highlight recurring mechanisms through which data-driven modeling can transform signal complexity from a limitation into an analytical resource under appropriate validation conditions. We also discuss critical challenges, including overfitting, validation design, interpretability, and reproducibility, which must be rigorously addressed to ensure sustainable progress. As analytical demands increasingly arise from complexity and variability rather than insufficient sensitivity alone, coupled sensor–model systems are emerging as a promising framework in which analytical performance is co-determined by physical transduction and information extraction.
Cimmino et al. (Mon,) studied this question.