Modern food systems require sensors that can detect hazards at the point of risk (i.e., on the line, in packaging, and along cold chains) before products reach consumers. This Research Topic aims to highlight breakthroughs in sensitivity, selectivity, and robustness, so that early and accurate detection becomes routine. We especially welcome advances that push detection limits for oxidants and other harmful residues, or markers of spoilage/adulteration within complex food matrices. The goals are to showcase: (i) new sensing materials and architectures that amplify signal while suppressing matrix interferences; (ii) surface transduction strategies (electrochemical, optical, hybrid) that enable rapid, calibration-lean quantification; (iii) best-practice metrics and protocols for real-world validation (response time, LOD/LOQ, drift, shelf-life, reusability, food-contact safety); and (iv) deployable formats (handhelds, inline probes, smart labels, and IoT-connected systems). By bridging fundamental materials innovation with manufacturable devices, this collection seeks to enhance food safety, streamline compliance, and strengthen consumer trust.

Food contamination and waste remain persistent global issues with costly recalls and public health impacts. Routine laboratory assays, while accurate, are often too slow, centralized, or labor-intensive for timely decision-making. Field-deployable sensors promise rapid screening at critical control points, yet current devices frequently struggle with low analyte levels, matrix effects (proteins, fats, pigments), and stability under variable temperature and humidity. Next-generation sensors that deliver early warning, continuous monitoring, and clear, actionable readouts are essential to modernize quality control and protect consumers.

We invite original research, reviews, mini-reviews, and perspectives on:

• Sensing materials (nano/biomimetic films, enzyme/polymer composites, MOFs, MXenes)
• Electrochemical, optical, and multimodal transduction
• Microfluidic and paper-based platforms
• Smart packaging indicators
• Ultra-low-level detection of oxidants, pesticides, adulterants, pathogens, and spoilage VOCs in complex matrices
• Data-centric methods (signal processing, chemometrics, machine learning) for interference rejection, drift correction, and calibration-light operation
• Validation in realistic settings: standardized protocols, interlaboratory comparisons, stability/shelf-life, sterilization, and food-contact safety
• Deployment/scale-up: IoT connectivity, cost, manufacturability, and regulatory readiness

Submissions should report LOD/LOQ, linear range, response/recovery times, selectivity panels, stability (short- and long-term), and user-relevant figures of merit to enable comparison and translation.

Are you interested in contributing a manuscript to this Research Topic?

Next-Generation Sensors for Improved Food Quality Control