Search

Ensuring food quality, safety, and authenticity has become a central challenge for contemporary food systems because risks now emerge across production, processing, distribution, retail, and consumption. Artificial intelligence is increasingly used to interpret complex food data, including spectral signals, images, sensor outputs, molecular profiles, traceability records, and supply-chain alerts. These developments suggest that AI can support a shift from reactive food inspection toward predictive and preventive food integrity assurance. Despite this promise, AI applications in food quality, safety, and authenticity often remain separated across disciplinary and technological domains. Sensing studies focus on detection accuracy, prediction studies focus on model performance, traceability studies focus on chain-of-custody infrastructure, and explainable AI studies focus on interpretability. This fragmentation limits the field’s ability to understand how these components could function together as an integrated assurance system. This integrative review synthesises 31 peer-reviewed articles published between 2017 and 2025 across food science, analytical chemistry, sensing technology, food informatics, and artificial intelligence. The review examines four interconnected domains: sensing technologies and data sources, prediction models for quality and safety, traceability and authenticity verification, and explainable decision systems. Its purpose is not to report new empirical data, but to integrate diverse research streams into a coherent conceptual understanding of AI-enabled food integrity assurance. The review shows that AI has already demonstrated value in optical sensing, spectroscopy, imaging, authenticity testing, shelf-life prediction, risk assessment, fraud detection, and traceability analysis. However, most applications are still designed as isolated models rather than as connected decision systems. Three tables classify sensing technologies, categorise prediction models, and present an integrative framework linking detection, prediction, provenance, and explainable decisions. The central conclusion is that the technical components of AI-enabled food integrity assurance already exist, but they remain insufficiently integrated. Future research should move beyond stand-alone classification or prediction models and toward interoperable, explainable, and operationally validated systems. Such systems should connect sensing, prediction, traceability, and human decision-making across food supply chains.

Cold chains are essential food-system infrastructures because they preserve safety, quality, nutritional value, and marketability from production to consumption. Despite their importance, many cold-chain operations still depend on reactive checks, delayed audits, and fixed temperature protocols that are poorly aligned with the biological variability of food products. This reactive orientation allows quality deterioration to accumulate invisibly until products arrive at inspection points, retailers, or consumers. The central problem addressed in this article is the absence of an integrated architecture that connects continuous monitoring, predictive analytics, and automated decision support across the full cold-chain pathway. Sensors may detect temperature excursions, models may estimate shelf life, and logistics systems may manage routing, yet these capabilities often remain fragmented. Without architectural integration, cold chains cannot become truly intelligent, adaptive, or self-correcting. The objective of this article is to propose an Intelligent Cold Chain conceptual architecture for real-time quality preservation, spoilage prediction, and loss prevention. The architecture is designed to transform environmental and logistical data into predictive quality intelligence and operational decisions. It positions the cold chain as a cyber-physical system in which sensing, prediction, decision-making, and coordination interact continuously. The article develops its architecture through a conceptual synthesis of peer-reviewed research published between 2017 and 2025. The synthesis draws from cold-chain logistics, food quality engineering, IoT monitoring, machine learning, digital twins, and supply-chain systems design. It does not report new empirical data, but instead integrates existing technological and theoretical insights into an original systems framework. The proposed architecture defines four functional layers: sensing, prediction, decision, and coordination. These layers specify how sensor streams are acquired, how spoilage risk is inferred, how corrective actions are recommended, and how information is shared across actors. The architecture provides a blueprint for moving cold chains from passive refrigerated corridors toward proactive, data-driven systems that preserve quality and reduce food losses.