渔业研究2024，Vol.46Issue(5)：547-562,16.DOI:10.14012/j.jfr.2024061
水产品货架期质量预测模型的研究进展
Recent advances in shelf life prediction models for monitoring aquatic product quality
摘要
Abstract
[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 ac-curate 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 ana-lysis,this paper offers a comprehensive overview of the application backgrounds and characteristics of com-monly used models.Special emphasis is placed on their specific applications within the seafood sector,particu-larly models predicting food freshness indices and shelf life.By comparing the strengths and weaknesses of dif-ferent 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 applica-tion 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 integ-ration 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.关键词
水产品/质量监测/货架期预测/数学模型/综述Key words
aquatic produc/quality monitoring/shelf life prediction/mathematical models/review分类
农业科技引用本文复制引用
董浩,郑诗伟,崔方超,王当丰,李婷婷,励建荣..水产品货架期质量预测模型的研究进展[J].渔业研究,2024,46(5):547-562,16.基金项目
辽宁省海洋经济发展专项(2021-84) （2021-84）
