Electrochemical Sensors for Quality Control in the Food and Beverage Industry
Abstract
Ensuring food safety and quality has become increasingly critical due to the rising complexity of the global food supply chain and the growing incidence of foodborne illnesses. This review aims to explore the potential of electrochemical sensors, enhanced with nanomaterials and artificial intelligence (AI), as efficient tools for detecting contaminants, additives, and adulterants in food and beverages. Traditional analytical techniques such as chromatography, spectroscopy, and titration though accurate, are often expensive, labour-intensive, and unsuitable for on-site or real-time analysis. In contrast, electrochemical sensors offer a rapid, low-cost, and portable alternative capable of providing accurate detection with minimal sample preparation. The incorporation of nanostructured materials like carbon-based nanomaterials, metal nanoparticles, and conductive polymers significantly boosts their sensitivity and selectivity by enhancing electron transfer and surface area. Furthermore, the integration of AI and machine learning (ML) techniques addresses major limitations of electrochemical sensors, including signal interference, overlapping analyte responses, and sensor drift. Algorithms such as SVM, CNNs, PCA, and PLSR enable multi-analyte detection, signal classification, and predictive maintenance, making real-time monitoring in complex food matrices feasible. Despite these advancements, challenges persist, including sensor fouling, matrix effects, lack of reproducibility,…
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