Mechanism-driven artificial intelligence for food cold-chain control and governance
4 affiliations
- 1Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- 2Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- 3School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- 4Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland
Abstract
Background Food cold chains are critical infrastructures for safeguarding perishable foods, yet prevailing management strategies remain largely rule-based, relying on sparse environmental monitoring, conservative safety margins and endpoint inspection. Such approaches insufficiently capture product-level micro-environment variability and deterioration kinetics, resulting in recurrent temperature abuse, avoidable food loss, excessive energy consumption and elevated greenhouse-gas emissions. Scope and approach This review advances a mechanism-driven perspective on artificial intelligence (AI) in food cold-chain systems. Rather than treating AI as a generic optimisation tool, we examine how near-product, multimodal sensing can be coupled with kinetics-informed and physics-aware models to infer remaining shelf life, safety margins and quality risk in real time. We synthesise evidence on how these mechanism-linked outputs inform dynamic process control, quality-based logistics allocation, first-expired–first-out implementation and energy-aware operation. Particular emphasis is placed on model interpretability, uncertainty quantification, external validation and clearly defined applicability domains as prerequisites for trustworthy deployment. Key findings and conclusions Across reported studies, AI demonstrates tangible value only when tightly anchored to measurable micro-environments and explicit deterioration mechanisms, and when embedded within governed decision loops aligned with formal food-safety management systems. We distil core design principles for mechanism-driven cold-chain intelligence: robust sensing and calibration; physics-informed feature construction; uncertainty-aware modelling; transparent validation across products and scenarios; and controlled transferability. Under these conditions, AI-enabled systems can transition cold chains from static compliance frameworks to dynamic, risk-based governance structures, with realistic potential to reduce avoidable losses, optimise energy demand and lower emissions while enhancing transparency and accountability in cold-chain performance.
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BibTeX
@article{xiong2026mechanismdriven,
title = {{Mechanism-driven artificial intelligence for food cold-chain control and governance}},
author = {Siguo Xiong and Zhiwei Zhu and Da‐Wen Sun},
journal = {Trends in Food Science \& Technology},
year = {2026},
month = may,
doi = {10.1016/j.tifs.2026.105824},
url = {https://doi.org/10.1016/j.tifs.2026.105824},
note = {CC BY},
}
RIS
TY - JOUR
TI - Mechanism-driven artificial intelligence for food cold-chain control and governance
AU - Siguo Xiong
AU - Zhiwei Zhu
AU - Da‐Wen Sun
PY - 2026
DA - 2026/05/20
JO - Trends in Food Science & Technology
DO - 10.1016/j.tifs.2026.105824
UR - https://doi.org/10.1016/j.tifs.2026.105824
AB - Background Food cold chains are critical infrastructures for safeguarding perishable foods, yet prevailing management strategies remain largely rule-based, relying on sparse environmental monitoring, conservative safety margins and endpoint inspection. Such approaches insufficiently capture product-level micro-environment variability and deterioration kinetics, resulting in recurrent temperature abuse, avoidable food loss, excessive energy consumption and elevated greenhouse-gas emissions. Scope and approach This review advances a mechanism-driven perspective on artificial intelligence (AI) in food cold-chain systems. Rather than treating AI as a generic optimisation tool, we examine how near-product, multimodal sensing can be coupled with kinetics-informed and physics-aware models to infer remaining shelf life, safety margins and quality risk in real time. We synthesise evidence on how these mechanism-linked outputs inform dynamic process control, quality-based logistics allocation, first-expired–first-out implementation and energy-aware operation. Particular emphasis is placed on model interpretability, uncertainty quantification, external validation and clearly defined applicability domains as prerequisites for trustworthy deployment. Key findings and conclusions Across reported studies, AI demonstrates tangible value only when tightly anchored to measurable micro-environments and explicit deterioration mechanisms, and when embedded within governed decision loops aligned with formal food-safety management systems. We distil core design principles for mechanism-driven cold-chain intelligence: robust sensing and calibration; physics-informed feature construction; uncertainty-aware modelling; transparent validation across products and scenarios; and controlled transferability. Under these conditions, AI-enabled systems can transition cold chains from static compliance frameworks to dynamic, risk-based governance structures, with realistic potential to reduce avoidable losses, optimise energy demand and lower emissions while enhancing transparency and accountability in cold-chain performance.
SN - 0924-2244
SN - 1879-3053
ER -