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Abstract The persistence of pesticide residues in the global food supply chain requires rapid, reliable, and continuous measurement methods to ensure consumer safety and regulatory compliance. Conventional analytical techniques provide exceptional precision. However, their destructive nature, high operational costs, and strict reliance on laboratory environments make them unsuitable for real-time screening in food processing facilities. This review examines the latest advancements in non-destructive electronic sensing technologies specifically designed for characterizing pesticide residues in post-harvest food matrices. The analysis focuses on the analytical capabilities of electrochemical sensors, biosensors, electronic nose (e-nose) platforms, and optical measurement systems, while critically evaluating how these platforms handle complex food matrix interferences, particularly background volatile organic compounds (VOCs), moisture fluctuations, and matrix-dependent signal variability. Particular attention is given to signal analysis and processing strategies, including spectral preprocessing, chemometric modelling, feature extraction, pattern recognition, sensor-drift compensation, and machine learning (ML)-based classification of pesticide signatures. Furthermore, the deployment of edge computing architectures is analyzed for its role in enabling decentralized, real-time signal processing directly on the production line. To establish practical viability for the food industry, the limits of detection achieved by various electronic configurations are benchmarked against international maximum residue limits. Finally, the paper addresses persistent hardware and methodological limitations, including sensor drift, calibration transferability, and reproducibility under highly humid food storage environments. By synthesizing these engineering challenges with food quality requirements, this review provides a comprehensive roadmap for implementing next-generation measurement systems in smart food supply chains.
Ayrancı et al. (2026) studied this question.