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.

Pesticide residue analysis of agricultural produce is vital because of associated health concerns, highlighting the need for effective non-destructive techniques. This study introduces a method that combines short-wavelength infrared hyperspectral imaging with spectral unmixing to detect chlorfenapyr and azoxystrobin residues on perilla leaves. Sixty-six leaves were treated with pesticides at concentrations between 0 and 0.06%. The study utilized multicurve resolution-alternating least squares (MCR-ALS), a spectral unmixing method, to identify and visualize the distribution of pesticide residues. This technique achieved lack-of-fit values of 1.03% and 1.78%, with an explained variance of 99% for both pesticides. Furthermore, a quantitative model was developed that integrates insights from MCR-ALS with Gaussian process regression to estimate chlorfenapyr residue concentrations, resulting in a root mean square error of double cross-validation (RMSEV) of 0.0012% and a double cross-validation coefficient of determination (R²v) of 0.99. Compared to other chemometric approaches, such as partial least squares regression and support vector regression, the proposed integrated method decreased RMSEV by 67.57% and improved R²v by 2.06%. The combination of hyperspectral imaging with spectral unmixing offers advancements in the real-time monitoring of agricultural product safety, supporting the delivery of high-quality fresh vegetables to consumers.