AI-Driven Nondestructive Measurement Technologies for Meat Quality and Safety: A Review

Meat quality and safety are critical aspects of global food security. However, traditional evaluation techniques, including sensory analysis and chemical and instrumental tests, are constrained by subjectivity, high time consumption, their destructive character, and susceptibility to bias. With rapid advances in sensor technologies and computational methods, there is a growing demand for the nondestructive, accurate, and fast measurement of meat quality and safety attributes. In recent years, artificial intelligence (AI) integrated with nondestructive sensing has emerged as a transformative paradigm, offering unparalleled capabilities for extracting quality information from complex datasets generated by various nondestructive sensing technologies. This review provides a comprehensive analysis of AI-driven nondestructive technologies for meat quality and safety assessment, focusing on the integration of machine learning and deep learning with various sensing techniques. Additionally, the review evaluates state-of-the-art algorithms and their performances and identifies deployment barriers, particularly calibration transfer, environmental sensitivity, reproducibility issues, sensor fouling, and generalization challenges across batches and processing plants. Furthermore, economic and regulatory constraints, including high sensor costs, small and medium enterprise (SME) adoption challenges, and alignment with HACCP/ISO frameworks that further limit commercial scalability, are discussed. Unlike previous reviews that primarily focus on individual sensing techniques, this review emphasizes the practical challenges associated with industrial implementation and the development of scalable solutions for real-world deployment. Finally, strategic research priorities and recommendations are highlighted to accelerate the industrial adoption of intelligent meat quality monitoring systems across the global meat industry.