Modelling, responses and applications of time-temperature indicators (TTIs) in monitoring fresh food quality

冷链 环境科学 保质期 食物链 生化工程 食品质量 计算机科学 食品科学 工艺工程 生物 工程类 生态学
作者
Tingting Gao,You Tian,Zhiwei Zhu,Da‐Wen Sun
出处
期刊:Trends in Food Science and Technology [Elsevier]
卷期号:99: 311-322 被引量:171
标识
DOI:10.1016/j.tifs.2020.02.019
摘要

Abstract Background Real-time temperature monitoring during cold chain logistics is critical because refrigerated foods are sensitive to temperature fluctuations. Time-temperature indicators (TTIs) are effective tools for monitoring the temperature history of food products in real-time. These technologies have been widely used in fresh food management, showing great potential to reduce food waste, ensure food safety, and achieve realistic control of the cold chain and cold storage of foods. Scope and approach This review presents commonly used modelling methods for TTIs establishment, including reaction rate constant (k) and activation energy (Ea) determination and Ea matching. Through mathematical modelling, shelf-life of TTIs can be matched with shelf-life of target foods, making response changes of TTIs reflect food quality. For the first time, different apparent response types of TTIs are summarized, covering colour-based, acidity-based, diffusion length-based, and other responses-based TTIs. Besides, TTIs applications in cold chain management of fresh foods are also highlighted concerning dairy, meat and aquatic products, and vegetables. Key findings and conclusions TTIs are effective temperature-sensitive monitoring tools and have been studied for monitoring various refrigerated fresh food products, especially for meat and aquatic products. Arrhenius equation is the most widely used mathematical modelling for matching kinetics of the food products and the TTI responses. Colour-based responses increasingly become the most popular response types of TTIs for the advantages of convenient and direct observation. The future trends of TTIs development should mainly focus on the integration of multiple quality indicators, constituent simplification, cost reduction, and modelling optimization.
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