## 4 Application of AI in Food Integrity
### AI and ML in Food Supply Chain Traceability  
The US FDA Food Safety Modernization Act (FSMA) addresses the need for rapid and effective food tracking and tracing [60]. Food traceability refers to the ability to systematically track (both backward and forward) the origin, location, and transformations of a food product and its ingredients at any stage of the supply chain, including manufacturing, processing, transport, distribution, and storage. Globalization and the increased complexity of the food supply chain, characterized by numerous actors and logistical steps, coupled with recent high-profile fraud and safety incidents, have fueled a heightened demand for transparency in food provenance and traceability [61]. Traceability helps consumers, food companies, inspectors, and government agencies to locate the source of a food product, assess the magnitude of potential food safety issues, and determine when and where such issues may have occurred, aiding in faster elimination of the affected product from the supply chain, thus minimizing public health situations. Traceability also ensures compliance with guidelines, reduces risks, promotes trust and responsible sourcing, improves supply chain efficiency and transparency, and enhances fair competition. Against the backdrop of novel technological inventions such as blockchain, 5G, big data, and IoT, food traceability in the Industry 4.0 age seems to greatly prosper, addressing critical vulnerabilities to fraudulent activities in the food supply chain that have heretofore remained elusive. The incorporation of these technologies presents novel opportunities for attaining more intelligent food traceability. AI, which is based on the principles of data science, is compatible with blockchain's data storage approach, such as \"Hyperledger\", as it embodies data intelligence. The effectiveness of the convergence of these technologies is attributed to their collective emphasis on data. The application of blockchain's data decentralization, immutability, consensus algorithms, and smart contracts can enhance the development of reliable AI systems [62]. An example in point is the collaboration between Walmart and IBM to implement a food traceability system based on the Hyperledger Fabric, allowing for swift source tracking of agricultural commodities within seconds. In China, this technology was utilized to track pork in the supply chain [63]. Furthermore, IBM, Walmart, and JD.com, in partnership with Tsinghua University National Engineering Laboratory for E-Commerce Technologies, declared the establishment of the Blockchain Food Safety Alliance [64]. The solution, created by IBM's blockchain platform, has a global scope and involves major participants such as Dole, Nestle, and Tyson Foods. Its objective is to enhance efficiency, transparency, and authenticity in food supply chains on a global scale [64]. [PERSON] et al. [65] presented a food traceability system for perishable foods based on blockchain, ML, and fuzzy logic. This novel system is called the Blockchain Machine Learning-based Food Traceability System (BMLFTS) and it incorporates a shelf-life management component. According to the authors, the proposed food traceability system enhances the efficiency of the current supply chain environment and produces reliable tracking, monitoring, and food quality outcomes. [PERSON] et al. [66] investigated the geographical origin of refined sugar using high-resolution mass spectrometry and the SVM algorithm, with a demonstrated classification accuracy of 83.3%. In a different study, the geographical origin of Saanen goat milk from Guangdong, Shaanxi, and Inner Mongolia Provinces in China was traced using mass spectrometric data and ML (OPLS-DA) modeling [67]. [PERSON] et al. [68] successfully employed a lipidomics technique in combination with PCA and partial least squares-discriminate analysis (PLS-DA) to determine the geographical origin of _Crassostrea gigas_ oyster species from three different maritime locations in China. In another study, [PERSON] et al.