In the 21st-century food safety challenges remain a hurdle since incidents all over the world are constantly reported. Foodborne pathogens, chemical pollutants, poor hygiene practices, and environmental hygiene are major threats to public health.

Therefore, scientists are focused on developing innovative technologies for the rapid detection of contaminants in foodstuffs. Besides advanced lab-based techniques for detection of contaminants, food safety experts and public health authorities are continuously investigating appropriate preventive measures to minimize the hazards throughout the supply chain and to ensure existing food safety management systems are regularly updated.

There are a variety of predictive models that can enhance food safety across the supply chain. Mathematical models for predictive microbiology such as kinetic, probability, empirical and mechanistic models have become invaluable for quantitative risk assessments in food industry. These models can provide information about the ecology and behaviour of foodborne pathogens, spoilage microorganisms, toxins or even about processes and treatments in the production line. An Italian web-based application for predictive microbiology, Praedicere Possumus, a model for prediction of spoilage bacteria in soybean, and predictive analysis for the distribution performance in the dairy industry is one such model which aims to mitigate potential risks.

ΑΙ technologies have emerged as a tool that can support practices for safe and sustainable food products. Through AI technology, a “knowledge-based system” can be constructed based on data from a variety of sources. Computers can utilize all of this information and imitate the decision-making abilities of food safety experts. AI methodologies utilize specific techniques for model construction. The most common predictive analysis models are clustering, classification, forecast, outliers, and time series models which include two well-known predictive algorithms: machine learning and deep learning.

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