Deep Learning Models for Shelf Life Prediction and Regulation of Various Foods: A Systematic Review.

Abstract

Accurate prediction of food shelf life is critical for ensuring food safety, reducing waste, and delivering reliable products to consumers. Deep learning, as an advanced artificial intelligence (AI) technology, provides transformative solutions for shelf life prediction. This paper systematically reviews research advances in deep learning applications for food shelf life prediction and regulation, including examinations of predictive model architectures, analyses of food quality assessment criteria, explorations of hybrid methods integrating data-driven and mechanistic approaches, and proposals for model-informed optimization strategies. With advancing AI, deep learning will further strengthen food safety systems, enhance resource efficiency, reduce waste, and modernize perishable goods supply chains. PRACTICAL APPLICATIONS: This paper explores the research and application of deep learning in the field of food shelf life, focusing on three key areas: common deep learning models, methods for evaluating preservation quality, and shelf-life prediction techniques. Additionally, the paper introduces the concept of reverse regulation of shelf life, offering innovative solutions for ensuring food safety, enhancing production efficiency, and integrating intelligent supply chain logistics.