Advances in Freezing and Thawing Meat: From Physical Principles to Artificial Intelligence

TL;DR

This review explores new freezing and thawing technologies, including AI, to improve meat quality and efficiency in the food supply chain.

Contribution

The paper introduces an integrated framework combining physical principles, advanced freezing methods, and AI for meat processing.

Findings

Emerging multi-physics methods better control ice-crystal formation, reducing cellular damage in meat.

AI applications show promise in enhancing freezing and thawing efficiency through simulation and predictive modeling.

Conventional freezing techniques are limited in their ability to regulate ice morphology effectively.

Abstract

With the sustained expansion of global meat consumption, advanced freezing and thawing technologies have become essential to preserve quality and extend shelf life within the food supply chain. This review systematically consolidates recent progress by examining fundamental principles, conventional techniques, emerging multi-physics methods (e.g., high-pressure-, ultrasound-, and electric field-assisted processing), and the integration of artificial intelligence (AI). It details the mechanism of ice-crystal formation and its impact on meat quality attributes. While conventional methods remain prevalent, their limitations in controlling ice crystallization are evident. Emerging technologies demonstrate superior capability in regulating ice morphology, thereby mitigating cellular damage. AI applications, including numerical simulation, quality monitoring via machine learning, and predictive modeling of thawing parameters, show considerable potential to enhance processing efficiency—though challenges in data scarcity and model generalizability remain. Collectively, these advancements form an integrated “theory–technology–intelligence” framework, supporting the development of more sustainable, efficient, and quality-focused meat processing systems.

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