## 4 Application of AI in Food Integrity
### AI and ML in Real-Time Monitoring of Food in the Supply Chain  
With the decentralized nature of the food supply chain, which presents difficulties for governmental oversight, the integration of AI and IoT in these supply enterprises offers peculiar benefits for real-time monitoring and assessment of food product quality and safety. Real-time monitoring constitutes the continuous and instantaneous collection, analysis, presentation, and reporting of data as events unfold. It entails the utilization of technologies and systems that offer prompt or nearly instantaneous feedback on a process, system, or environment. The main objective of real-time monitoring is to facilitate timely decision making, intervention, or response using the most current information accessible. With the integration of AI and ML technologies, real-time monitoring often involves the use of smart sensors and other data collection devices at critical stages of the supply chain. The gathered data are thereafter transmitted to AI systems for immediate processing and predictive modeling with the goal of identifying any deviations from the specified standards, enabling prompt corrective measures, and improving overall process effectiveness and safety. [PERSON] et al. [81] developed and demonstrated an ML-enhanced RFID-based system in combination with sensors to track and monitor the temperature and humidity of perishable foods along the supply chain (i.e., during storage and transportation). In the study by [PERSON] et al. [82], an IoT-based solution was proposed for food safety and quality control. This system was evaluated via a pilot project in China known as the Internet of Agricultural Things (AIoT), which incorporated cutting-edge technologies to create a streamlined approach for monitoring and tracing the food supply chain, specifically targeting food safety issues. The data and information synthesized from the so-called AIoT system were presented in an easily accessible and user-friendly manner, allowing both consumers, suppliers, and supervisors to make well-informed decisions when purchasing and supervising food supplies, presenting a hopeful resolution to augment the transparency, traceability, and ultimately, the security of food supply chains. [PERSON] et al. [83] proposed a combination of advanced deep learning (ADL) and IoT-blockchain technologies to optimize the provenance of the food supply chain within the context of Food Industry 4.0. Their proposed system allowed food consumers to verify the origin and method of food distribution before consuming it, promoting transparency. The technology also ensures that goods are maintained at the proper temperature throughout the whole supply chain. An AI-powered early warning system based on an analytic hierarchy process integrated extreme learning machine (AHP-ELM) was proposed by [PERSON] et al. [84] to address the complexities and intricacies of food safety inspection data. The algorithm extracts effective process characteristic information (PCIs) from the inspection data by employing the analytic hierarchy process (AHP) model. The study concluded that the proposed AHP-ELM model was effective and feasible in processing complex food inspection data, contributing to improving the quality of food products, ensuring food safety, and reducing the overall risk of food safety incidents. [PERSON] [85] conducted a study showcasing a decentralized real-time food safety supply chain traceability system that integrated Hazard Analysis and Critical Control Points (HACCP), blockchain, and IoT in order to make information available to all participants and players along the food supply chain, effectively minimizing the hazards associated with centralized information systems and aiding in strengthening food safety protocols and restoring consumer trust in the food sector. [PERSON] et al. [86] investigated an ML-powered system for the real-time detection of foodborne illness in a real-world setting.