Nondestructive prediction of freshness for bighead carp (Hypophthalmichthys nobilis) head by Excitation-Emission Matrix (EEM) analysis based on fish eye fluid: Comparison of BPNNs and RBFNNs

文献类型： 外文期刊

第一作者： Shi, Xin

作者： Shi, Xin;Zhang, Jiaran;Shi, Ce;Shi, Xin;Tan, Yuqing;Hong, Hui;Luo, Yongkang;Shi, Xin;Zhang, Jiaran;Shi, Ce;Shi, Xin;Zhang, Jiaran;Shi, Ce

作者机构：

关键词： Bighead carp head; Freshness prediction; Excitation -Emission Matrix; Parallel factor analysis; Back-propagation neural networks; Radial basis function neural networks

期刊名称：FOOD CHEMISTRY （ 影响因子：9.231； 五年影响因子：8.795 ）

ISSN： 0308-8146

年卷期： 2022 年 382 卷

页码：

收录情况： SCI

摘要： This study established back-propagation neural networks (BPNNs) and radial basis function neural networks (RBFNNs) models for evaluating the freshness of bighead carp head storage at different temperatures via the characteristic components of Excitation-Emission Matrix (EEM). Two characteristic components of EEM data of fish eye fluid were extracted by parallel factor analysis (PARAFAC) and were the most efficient components to stimulate fluorophores responsible for fish freshness detection during variable temperatures. EEM-RBFNNs and EEM-BPNNs models based on characteristic components of EEM used to predict the fish freshness. The results demonstrated the relative errors of EEM-BPNNs models for hiobarbituric acid reactive substances (TBARS) and total viable bacteria count (TAC) prediction were less than 10% which were better than those of EEM-RBFNNs models. It indicated that EEM-BPNNs model of bighead carp eye fluid by PARAFAC has a high potential for predicting fish freshness under variable storage conditions.

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