小虾
食品科学
蛋白质降解
降级(电信)
相关系数
化学
食物腐败
生物
生物化学
渔业
细菌
机器学习
计算机科学
遗传学
电信
作者
Wenya Xu,Lijuan Wang,Jianfeng Sun,Yanlei Li,Jie Wang,Yiwei Tang,Yaqiong Liu,Jianlou Mu,Wenxiu Wang
标识
DOI:10.1016/j.jfca.2022.104773
摘要
Shrimp are highly prone to spoilage during refrigerated storage. Systematic assessment of shrimp protein degradation and quality loss during refrigerated storage is essential for optimizing storage of shrimps. In this study, protein biochemical characteristics and quality indicators of shrimp during refrigerated storage at 4 °C were assessed, and back-propagation artificial neural networks (BP-ANNs) were used to elucidate correlations of quality characteristics with protein degradation. With increasing storage time, Ca2+-ATPase activity, total amino acids, sulfhydryl group values, elasticity, and stickiness decreased, whereas protein carbonyl group values, total volatile base nitrogen content, and biogenic amine content increased. Furthermore, SDS-PAGE and transmission electron microscopy revealed protein degradation and damage to shrimp muscle microstructure during storage. With protein degradation characteristics as input values and quality indicators as output values, BP-ANNs models were established to analyze their correlation. A high correlation coefficient of 0.9654 and low mean square error of 0.0069 were obtained, indicating that BP-ANN can reliably illustrate the correlation between protein degradation and shrimp loss. Our results provided basic data and technical support for the intelligent storage of shrimp.
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