Applications of Artificial Intelligence and Machine Learning in Food Quality Control and Safety Assessment

To ensure food safety and uphold high standards, the food business must overcome significant obstacles. In recent years, promising answers to these issues have emerged in the form of artificial intelligence (AI) and machine learning (ML). This thorough review paper analyses the various uses of AI and ML in food quality management and safety evaluation, offering insightful information for academics, business people and legislators. The evaluation highlights the value of food quality assessment and control in consideration of growing consumer demand and regulatory scrutiny. The powerful capabilities of AI and ML are touted as having the potential to revolutionize these procedures. This study illustrates the numerous uses of AI and ML in food quality management through an in-depth exploration of these technologies. Defect detection and consistency evaluation are made possible using computer vision techniques, and intelligent data analysis and real-time monitoring are made possible by natural language processing. Deep learning techniques also provide reliable approaches for pattern recognition and anomaly detection, thus maintaining consistency in quality across manufacturing batches. This review emphasizes the efficiency of AI and ML in detecting dangerous microorganisms, allergies and chemical pollutants with regard to food safety evaluation. Consumer health risks are reduced because of the rapid identification of safety issues made possible by integrating data from diverse sources, including sensors and IoT devices. The assessment discusses issues and restrictions related to the application of AI and ML in the food business while appreciating the impressive progress that has been made. Continuous efforts are being made to improve model interpretability and reduce biases, which calls for careful evaluation of data quality, quantity and privacy issues. To assure compliance with food safety norms and regulations, the article also covers regulatory approval and validation of AI-generated outcomes. The revolutionary potential of AI and ML in raising food industry standards and preserving public health is highlighted on future perspectives that concentrate on new trends and potential innovations. This comprehensive review reveals that the integration of AI and ML technologies in food quality control and safety not only enhances efficiency, minimizes risks and ensures regulatory compliance but also heralds a new era of personalized nutrition, autonomous monitoring and global collaboration, signifying a transformative paradigm in the food industry.