Integrating machine learning with electrochemical sensors for intelligent food safety monitoring

The integration of machine learning (ML) with electrochemical sensors is transforming food safety and quality assessment by enabling quick, affordable, and highly sensitive detection of contaminants, adulterants, and spoilage indicators. Traditional electrochemical analysis faces challenges such as overlapping signals, nonlinear sensor responses, and matrix effects, which diminish accuracy and scalability. ML algorithms offer advanced data processing, feature extraction, and predictive modeling, significantly enhancing detection sensitivity, classification accuracy, and supporting real-time decision-making. This review explores the combined use of ML and electrochemical sensing in food analysis, focusing on key areas like pesticide and heavy metal detection, food authentication, shelf-life prediction, and microbial safety monitoring. It provides a comprehensive range of ML techniques, from basic algorithms like Support Vector Machines and Random Forests to advanced deep learning architectures, including Convolutional Neural Networks, Transformers, and Graph Neural Networks. Additionally, it highlights innovative applications and addresses critical challenges in real-world deployment, such as data scarcity, model generalizability, and the “black box” problem of interpretability. Strategies such as data augmentation, transfer learning, and explainable AI (XAI) are emerging as crucial solutions to enhance data availability and model transparency. The field is also advancing toward adaptive learning frameworks and integration with the Internet of Things (IoT), enabling continuous, networked monitoring throughout the food supply chain. By emphasizing both technical innovations and practical challenges, this review offers a solid foundation for researchers and professionals working at the intersection of electrochemical sensing, machine learning, and food safety analytics. • Reviews integration of machine learning with electrochemical sensors for food analysis. • Explains how AI improves signal interpretation, selectivity, and detection accuracy. • Summarizes ML algorithms from SVM and RF to CNN, Transformer, and GNN models. • Demonstrates applications in pesticide, heavy metal, and adulteration detection. • Discusses data scarcity, model interpretability, and real-world deployment challenges.