Background Annually, approximately 35% of global seafood is lost or wasted during the journey from catch to consumption. This substantial loss not only incurs significant economic costs but also adversely impacts the environment.

Objective To mitigate these effects, it is essential for manufacturers to provide accurate information regarding the shelf life of seafood at every stage of the supply chain. Reliable shelf life data is crucial for optimizing supply chain management, enhancing food safety, and reducing waste. This article aims to categorize and analyze existing shelf life models, detailing their applications within the seafood industry. By doing so, it seeks to assist stakeholders in better understanding and utilizing these models, ultimately improving seafood quality monitoring and management.

Methods Through an extensive literature review and case analysis, this paper offers a comprehensive overview of the application backgrounds and characteristics of commonly used models. Special emphasis is placed on their specific applications within the seafood sector, particularly models predicting food freshness indices and shelf life. By comparing the strengths and weaknesses of different models, this study explores their effectiveness and potential in practical applications.

Conclusion The findings indicate that a wide variety of models are currently employed for monitoring food quality, each with its distinct application context and characteristics. Shelf life models commonly used include kinetic models, neural networks, accelerated shelf life testing, and partial least squares regression models. In the seafood industry, these models are extensively used to predict the shelf life of fish, shellfish, and crustaceans, aiding enterprises in optimizing supply chain management and reducing losses. Future research should focus on further promoting the use of shelf life models in the seafood industry, particularly the development of new models and the application of multivariate analysis methods.

Prospect  Real-time food quality monitoring can identify more reliable methods of transportation, processing, and packaging, thus reducing losses and enhancing efficiency. The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies can facilitate real-time monitoring and prediction. These advancements can significantly reduce losses and waste at various stages of the seafood supply chain, and improve the economic efficiency and sustainability of the entire industry.