Advancing Food Safety Sensing through Artificial Intelligence: Machine Learning-Enhanced Biosensors in Action ^†

Current food safety techniques and equipment are struggling to meet the evolving demands of the food industry. Traditional practices rely on reactive measures, leading to delays in monitoring, early warnings, and risk assessments, thereby impeding their effectiveness in risk mitigation. The integration of nanotechnology and biosensors into food sensing offers significant advantages, including enhanced speed, cost-effectiveness, and on-site detection, surpassing the capabilities of larger analytical tools. This integration is pivotal for the early detection of pathogens, the effective control of fresh food, and the prevention of food-borne illnesses by identifying spoilage before it reaches consumers. Nevertheless, biosensors based on antibodies or aptamers face limitations in lifetime and stability that impact their commercial viability. To overcome these challenges, researchers are turning to artificial intelligence as a groundbreaking solution. The application of machine learning, also known as deep learning, has the potential to transform conventional biosensors into intelligent systems capable of automated analyte prediction through a decision-making process. This facilitates the control of harmful substances during food traceability processing. However, this innovative convergence has raised ethical and privacy concerns that demand careful consideration [1,2,3,4,5]. This review evaluates the integration of artificial intelligence into biosensors, aiming to create cost-effective, real-time recognition devices for the identification of contaminants in food matrices.