## 4 Application of AI in Food Integrity
### AI and ML in Food Quality Control and Regulatory Compliance  
Relevant players in the food value chain are progressively embracing AI and ML technologies to enhance safety inspection and quality control operations, as well as optimize their operations in order to minimize errors and deviations from established quality standards. A case in point is that of AgShift [49], a US-based food company that implements an AI-powered automated food inspection system. The company's primary product, the \"Hydra\" platform, utilizes AI, IoT, and computer vision to automate and standardize evaluations of food quality. The system employs deep learning techniques in an automated workflow to examine photos of food products, detecting flaws and evaluating them to guarantee adherence to industry standards and regulations. This approach improves the efficiency of inspections, decreases human errors, upholds consistent quality control, and empowers food firms to limit waste and provide safer superior products to consumers [49; 50]. Figure 2: Dimensions of AI and ML in food integrity. Computer vision, one of the critical technologies implemented by AgShift, facilitates the extraction of intricate insights from digital images, videos, and other visual inputs that are consumed and comprehended by AI algorithms that use such to make informed judgments and take suitable actions. In food processing, computer vision is employed for quality control by identifying defects, ensuring proper labeling, and assessing overall product quality. Manual food quality inspections and quality control in the process stream are suboptimal in many ways. On the one hand, the task is frequently perceived as monotonous, demanding sustained attention from individuals for extended durations, a cognitive aspect in which human brains are notoriously deficient. Nonetheless, with the aid of AI and ML technologies, automated systems are able to establish accurate and consistent quality characteristics of different food products ranging from differences in shapes, sizes, colors, textures, and nutraceutical compositions of different food commodities and maintain quality during processing. [PERSON] et al. [51] implemented an ML-driven computer vision system to discriminate defective rice grains with flaws such as fracture, chalkiness, or spotting. The method employed near-infrared pictures and a support vector machine (SVM) classifier for computer intelligence and decision making. The results of the study indicated that the system was very effective, with an accuracy of 99.3% in identifying fractured kernels, a 96.3% accuracy in identifying chalky grain regions, and a 93.6% accuracy in identifying impaired/spotted grain areas, all within a record average execution time of 0.15 s. [PERSON] [52] proposed a Mamdani fuzzy inference system (MFIS) as a decision-making tool for categorizing and grading Mozafati dates according to three quality parameters: juice quantity, size, and freshness. Their ML-based grading method demonstrated an 86% conformity with the human-based grading method. Moreso, the model was more accurate than that of human specialists in date classification. [PERSON] et al. [53] developed an ML-powered computer vision system that accurately identified and graded tomatoes based on visual appearance (from RGB images). For defect detection, the system deployed a radial basis function (RBF) kernel-SVM classifier, which effectively detected calyx and stalk scars, achieving an accuracy of nearly 95% for both defect-free and damaged tomatoes. Overall, the system demonstrated great promise as an inline tomato sorting tool, capable of ensuring adherence to quality standards while streamlining the process. Another computer vision approach being implemented for food quality inspection is hyperspectral imaging for compositional analysis of food products, which does not require any form of sample preparation [54]. The utilization of spectral signatures of different chemical constituents facilitates the process of composition mapping in food materials and products.