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

Area of Science:

• Food Science and Technology
• Agricultural Engineering
• Artificial Intelligence in Food Safety

Background:

• Traditional meat quality and safety evaluations are subjective, time-consuming, destructive, and prone to bias.
• Growing demand for rapid, accurate, and nondestructive methods for meat quality and safety assessment.
• Limitations of conventional methods necessitate advanced technological solutions.

Purpose of the Study:

• To comprehensively review AI-driven nondestructive technologies for meat quality and safety assessment.
• To analyze the integration of machine learning (ML) and deep learning (DL) with various sensing techniques.
• To identify practical challenges and propose scalable solutions for industrial implementation.

Main Methods:

• Review of AI (ML/DL) integrated with nondestructive sensing technologies (e.g., spectroscopy, imaging).
• Evaluation of state-of-the-art algorithms and their performance metrics.
• Analysis of deployment barriers and economic/regulatory constraints for commercial scalability.

Main Results:

• AI-powered nondestructive sensing shows significant potential for accurate meat quality and safety monitoring.
• Key challenges include calibration transfer, environmental sensitivity, reproducibility, sensor fouling, and generalization.
• Economic factors like sensor costs and SME adoption, alongside regulatory alignment, hinder widespread implementation.

Conclusions:

• AI-driven nondestructive sensing is a transformative approach for the meat industry.
• Addressing practical implementation challenges is crucial for scalable, real-world deployment.
• Strategic research priorities are needed to accelerate the adoption of intelligent meat quality monitoring systems.