Cell-Based Electrochemical Biosensors in Food Safety: Cellular Responses, Interface Engineering, and Translational Perspectives

Abstract

Cell-based electrochemical biosensors (CBEBs) employ viable cells as recognition, response, or signal-amplification elements and convert cell-derived physiological or biochemical changes into electrochemical outputs. By integrating cellular responses such as metabolic remodeling, ion-channel modulation, oxidative stress, membrane disruption, and apoptosis, CBEBs provide functional biological information that complements conventional assays based on isolated enzymes, antibodies, or aptamers. This capability is particularly relevant to food-safety screening, where complex matrices and contaminant mixtures may induce combined biological effects that are difficult to assess through single-analyte detection alone. This review examines the design and application of CBEBs, with emphasis on the relationships among cellular responses, electrochemical transduction, and cell–electrode biointerfaces, and how food-matrix conditions jointly determine analytical performance. Amperometric, voltammetric, and impedimetric strategies are compared in terms of measurable biological endpoints, matrix sensitivity, and signal interpretability. Mammalian, microbial, immune, stem-cell-derived, and engineered cell models are discussed in relation to food-safety sensing requirements, together with interface-engineering approaches based on nanomaterials, conductive polymers, hydrogels, 3D scaffolds, and microfluidic systems. Food-safety studies are prioritized, while selected nonfood examples are considered only where they clarify transferable design principles. Finally, major barriers, including limited cell stability, signal variability, insufficient specificity, matrix interference, and a lack of standardized validation in representative food samples, are critically evaluated. Future progress will require reproducible cell–electrode interfaces, matrix-aware validation, integrated microfluidic control, and data-assisted signal interpretation to support robust and portable platforms for effect-based food-safety screening.