Recent advances in shelf life prediction models for monitoring food quality. Issue 2 (30th January 2023)

• Record Type: Journal Article
• Authors: Cui, Fangchao; Zheng, Shiwei; Wang, Dangfeng; Tan, Xiqian; Li, Qiuying; Li, Jianrong; Li, Tingting
• Is Part Of: Comprehensive reviews in food science and food safety. Volume 22:Issue 2(2023)
• Page Start: 1257
• Page End: 1284
• Publication Date: 2023-01-30
• Subjects: food spoilage -- multivariable data analysis -- neural networks -- shelf life prediction models -- virtual sample generation; Food -- Research -- Periodicals; Food -- Safety measures -- Periodicals; 664.0072
• DOI: 10.1111/1541-4337.13110
• Languages: English
• ISSNs: 1541-4337

Abstract

Abstract: Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real‐time food quality monitoring.