Rapid Electronic Sensing Technologies for Ensuring Food Safety Against Pesticide Residues: A Comprehensive Review

Abstract

Pesticide residues in fruits, vegetables, cereals, tea, edible oils and other agricultural commodities remain a major food safety and environmental concern. Conventional chromatographic methods provide high selectivity and quantitative reliability, but their dependence on laboratory infrastructure, sample preparation, skilled personnel and relatively long analytical workflows limits their use for high-throughput, on-site screening. Recent advances in electronic noses, electrochemical biosensors, optical and spectroscopic sensors, hyperspectral imaging, portable instrumentation and machine learning have created a complementary pathway for rapid, non-destructive or minimally destructive screening. This review synthesizes the technological landscape from 123 references, emphasizing the link between pesticide application, residue-related chemical signatures, sensor transduction, signal processing and data-driven decision making. Particular attention is given to electronic-nose approaches that detect volatile or semi-volatile signatures associated with pesticide contamination, as well as electrochemical and optical systems that provide more chemically specific responses. The review compares sensor modalities, recognition algorithms, reference analytical methods, matrix effects, calibration requirements and deployment barriers. The literature indicates that machine learning can improve discrimination of complex sensor patterns, but generalization remains constrained by sensor drift, humidity and temperature, cultivar and commodity differences, pesticide formulation effects, and limited external validation. A practical deployment framework is proposed in which electronic sensing is positioned primarily as a rapid screening and triage layer, while chromatographic methods remain important for confirmatory quantification and regulatory decisions. Future systems should integrate multimodal sensing, edge AI, standardized datasets, uncertainty estimation, traceability and field calibration to achieve reliable Agriculture 5.0 food-safety monitoring.