Chemical programmability in food-safety nanobiosensing: a pathway-centric framework for autonomous analytical systems

Food-safety sensors have achieved extraordinary gains in analytical sensitivity, yet their performance often deteriorates in real food matrices. We argue that this long-standing gap arises not primarily from insufficient sensitivity, but from a loss of control over signal-transduction pathways. Matrix components, ranging from proteins and lipids to pigments and endogenous nucleic acids, can divert chemical flux through competing recognition, transduction, and output routes, leading to systematic misidentification despite excellent laboratory performance. To address this challenge, we introduce the concept of chemical programmability, defined as the rational control of sensing pathways through pre-encoded chemical instructions that govern signal generation, transduction, amplification, and readout. We further establish a 3 × 2 programmability matrix based on two orthogonal dimensions: programming node (recognition, transduction, and output) and programming timing (design-time fixed and run-time dynamic). Representative strategies across molecular-layer recognition engineering, material-layer energy/electron-transfer control, and system-layer signal-to-decision conversion are systematically discussed. Despite their mechanistic diversity, these approaches share a common physical foundation: energy landscape engineering, in which target pathways are selectively favored through modulation of competing energy barriers. By shifting the focus from sensitivity optimization to pathway controllability, this Review provides a unified framework for designing robust, adaptive, and autonomous food-safety sensing systems.