Chemometrics reshapes the role of biosensors: from signal transducers to intelligent analytical systems
6 affiliations
- 1Department of Pharmacy, University of Naples Federico II, Naples, 80131, Italy
- 2Clinical and Translational Oncology Program, Scuola Superiore Meridionale, SSM, Naples, 80131, Italy
- 3Department of Public Health, University Federico II of Naples, Naples, 80131, Italy
- 4Bioelectronics Task Force at University of Naples Federico II, Via Cinthia 21, Naples, 80126, Italy
- 5Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
- 6Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, PA, 19122, USA
Abstract
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.
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BibTeX
@article{cimmino2026chemometrics,
title = {{Chemometrics reshapes the role of biosensors: from signal transducers to intelligent analytical systems}},
author = {Wanda Cimmino and Alessandra Glovi and Antonella Miglione and Umberto Malapelle and Stefano Cinti},
journal = {Biosensors and Biomolecular Electronics},
year = {2026},
month = jun,
doi = {10.1186/s44510-026-00005-4},
url = {https://doi.org/10.1186/s44510-026-00005-4},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Chemometrics reshapes the role of biosensors: from signal transducers to intelligent analytical systems
AU - Wanda Cimmino
AU - Alessandra Glovi
AU - Antonella Miglione
AU - Umberto Malapelle
AU - Stefano Cinti
PY - 2026
DA - 2026/06/15
JO - Biosensors and Biomolecular Electronics
DO - 10.1186/s44510-026-00005-4
UR - https://doi.org/10.1186/s44510-026-00005-4
AB - 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.
SN - 3091-387X
ER -