Jeong Seul-Ki-Chan, Jo Kyung, Lee Seonmin, Jeon Hayeon, Kim Soeun, Han Seokhee, Woo Minkyung, Choi Yun-Sang, Jung Samooel
Department of Animal Science and Biotechnology, Chungnam National University, Daejeon 34134, Korea.
Research Group of Food Processing, Korea Food Research Institute, Wanju 55365, Korea.
Food Sci Anim Resour. 2025 May;45(3):711-726. doi: 10.5851/kosfa.2025.e15. Epub 2025 May 1.
Freezing is a valuable technique for increasing the shelf-life of meat. However, various changes occur in the physicochemical properties of frozen meat, which are affected by the frozen storage conditions, including the freezing temperature and storage duration. Conventional methods for measuring the properties of frozen-thawed meat are destructive and time-consuming. Therefore, non-destructive real-time analyses have been developed. Non-destructive analyses are divided into spectroscopy- and imaging-based technologies. A combination of non-destructive methods and supervised learning has been used to predict the properties of frozen-thawed meat, such as lipid and protein oxidation, which are affected by frozen storage conditions. This review focuses on the changes in meat properties caused by temperature and storage duration in freezing conditions, and the non-destructive measurements used to analyze the properties of frozen-thawed meat.
冷冻是延长肉类保质期的一项重要技术。然而,冷冻肉的物理化学性质会发生各种变化,这些变化受冷冻储存条件的影响,包括冷冻温度和储存时长。传统的测量冻融肉性质的方法具有破坏性且耗时。因此,已开发出非破坏性实时分析方法。非破坏性分析可分为基于光谱学和成像的技术。非破坏性方法与监督学习相结合已被用于预测冻融肉的性质,如受冷冻储存条件影响的脂质和蛋白质氧化。本综述聚焦于冷冻条件下温度和储存时长引起的肉性质变化,以及用于分析冻融肉性质的非破坏性测量方法。