## 4 Application of AI in Food Integrity
### AI and ML in Predictive Analytics for Risk Assessment  
Through rigorous analysis of extensive food safety datasets, predictive AI and ML models can accurately estimate the probability of future occurrences and identify specific areas that require attention, as well as assist in developing effective food safety management systems [72]. This information is used to build interventions that allocate resources more effectively while helping food safety specialists make well-informed decisions to guarantee a more secure food supply chain [50; 72]. In the study by [PERSON] and [PERSON] [73], numerous ensemble learning models were utilized to forecast food safety hazards, with a specific emphasis on enhancing border inspection techniques for imported food in Taiwan. Online implementation of their approach models resulted in a notable improvement in the non-conformity hit rate, demonstrating the efficacy of ensemble learning in predicting food safety risks. In a different approach, [PERSON] [74] constructed a food safety risk intelligent early warning system leveraging the predictive prowess of SVM that can assess and signal food safety risks. [PERSON] et al. [75] created an ML-based automated food safety early warning system for the dairy supply chain with the goal of detecting indicators that precede the emergence of food safety hazards from adjacent domains of the food supply chain, referred to as \"drivers of change\". The European dairy supply chain was used as a case study during the implementation of the system. The authors adopted a Bayesian network to detect chemical food safety risks in milk. Throughout the study period (2008-2019), anomalies were regularly detected in the data across all nations examined, offering the opportunity for prompt preventive actions by food manufacturers or inspectors to tackle emerging food safety issues. In a different study, [PERSON] et al. [76] utilized an unsupervised ML model, specifically the Latent Dirichlet Allocation topic model, to analyze the Europe Media Monitor Medical Information System (EMM/MEDISYS) corpus in order to promptly identify instances of food fraud (beeswax adulteration) reported in the media. A total of 2276 news articles were collected and categorized into 10 subjects. Each topic showed a different level of connection to beeswax adulteration. The themes also indicated the potential for new hazards in the cosmetic and food packaging industries. Additionally, some of the themes emphasized the rise of fresh market prospects for beeswax. Overall, the adopted Latent Dirichlet Allocation topic model demonstrated abilities in efficiently analyzing vast media communications and creating targeted food fraud filters on EMM/MEDISYS, which could directly aid stakeholders responsible for monitoring, evaluating, and addressing food fraud instances. [PERSON] et al. [77] developed an AI model to accurately estimate the likelihood of food fraud events. The Bayesian network model developed in the study was derived from an analysis of 1393 instances of food fraud data from 15 distinct sources. The model successfully identified product categories with the greatest likelihood of being involved in fraudulent activities with a prediction accuracy of 91.5%. Elsewhere, a similar model was developed to forecast significant food safety risks and their corresponding food products, with an emphasis on herbs and spices [78]. The model's predictive accuracy exceeded 85%. [PERSON] et al. [79] investigated the use of an extreme learning machine (ELM) model to predict food safety risks in dairy products. This early-warning model implemented the kernel-based extreme learning machine (K-ELM), which achieved a prediction accuracy of 86%, showing its potential to guarantee the quality and safety of food products. [PERSON] et al. [80]evaluated different ML models, both neural and non-neural, for the prediction of food safety risks using data from the European Union Rapid Alert System for Food and Feed (RASFF).