###### Abstract  
Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system. artificial intelligence; machine learning; food integrity; food safety; food quality control; food hazards; nutritional health +
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safety and microbiological food hazards including morbidity and consequent immobility, disability, mortality, costs of diagnosis/treatment, etc., are difficult to accurately estimate but unequivocally prodigious. Moreover, the disproportionate impact of food safety on designated societal groups (e.g., children, the elderly, the poor, and other vulnerable members of society who bear the brunt of this effect) further exacerbates this dire situation. In response, government agencies and relevant health actors along the food supply chain often review food safety policies and tighten regulations in order to mitigate public health concerns. For example, recent high levels of ochratoxin A (OTA), a potent mycotoxin, in certain food products prompted the European Commission to take action by issuing Regulation (EU) 2022/1370 on 5 August 2022 [10], amending Regulation (EC) No. 1881/2006 and adding maximum levels (MLs) for OTA in certain foodstuffs. While previous regulations already established MLs for OTA in selected foods, the absence of limits for other food items contributing to human exposure necessitated this amendment. Additionally, the updated regulation introduced new MLs for various foodstuffs and tightened existing limits for specific food items. These changes aimed to enhance public health by ensuring stricter adherence to food processing and handling standards [10; 11; 12]. Indeed, food integrity poses a complex and multidimensional challenge and traditional methods of monitoring food quality and safety rely on manual inspection and testing, which can be time-consuming and prone to human error. To effectively address these concerns, food businesses must develop robust monitoring systems that can detect and prevent food quality issues early in the production process. This is where AI and ML technologies are emerging as a beacon of promise and a transformative frontier in addressing these critical challenges within the food system. AI and ML have the potential to tackle complex problems and unearth innovative solutions with real-life applications, including improving the accuracy, efficiency, and effectiveness of food safety and quality monitoring systems.