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Quality Analysis and Shelf-Life Prediction of Antarctic Krill (Euphausia superba) Sauce Based on Kinetic Model and Back Propagation Neural Network Model

文献类型： 外文期刊

作者： Chi, Hai 1 ; Zhang, Yuanxing 1 ; Zhao, Lukai 1 ; Lin, Na 1 ; Kang, Wei 1 ;

作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Shanghai 200090, Peoples R China

2.Dalian Ocean Univ, Coll Food Sci & Engn, Dalian 116023, Peoples R China

3.Univ Shanghai Sci & Technol, Sch Hlth Sci & Engn, Shanghai 200093, Peoples R China

期刊名称：JOURNAL OF FOOD PROCESSING AND PRESERVATION （ 影响因子：2.5； 五年影响因子：2.6 ）

ISSN： 0145-8892

年卷期： 2024 年 2024 卷

页码：

收录情况： SCI

摘要： The study is aimed at determining how the quality of Antarctic krill (Euphausia superba) sauce (AkS) changed over time, including changes in color, moisture content, acid value (AV), peroxide value (POV), thiobarbituric acid reactive substance (TBARS), aerobic plate count, and sensory score. Quality variations and shelf life of AkS were estimated using kinetic model and back propagation (BP) neural network model. The results showed that sensory score, moisture content, and a & lowast; values of AkS declined as storage temperature increased at 4, 25, and 37 degrees C. In addition, the L & lowast; values, b & lowast; values, AV, POV, and TBARS of AkS increased as storage duration increased, indicating that high storage temperature of the samples accelerated quality degradation. The primary reason for AkS degradation was the oxidation of proteins and lipids. The POV, TBARS, and total sensory evaluation rating exhibited a highly significant correlation, and therefore, POV and TBARS were selected as the indicators for the two models. The BP neural network outperformed the kinetic model in predicting quality changes over the whole storage period, with relative errors of less than 10%. In terms of shelf-life prediction, the BP neural network's relative errors were 11.76% and 13.39% in POV and TBARS, respectively. POV and TBARS had experimental shelf lengths of 119 and 142 d, respectively. Compared with the kinetic model, the BP neural network model predicted the quality changes and shelf life of AkS with greater accuracy and stability. The findings offer fundamental insights and innovative concepts for the production of high-value Antarctic krill products, as well as the exploitation of Antarctic krill resources.

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