Deep learning models with optimized fluorescence spectroscopy to advance freshness of rainbow trout predicting under nonisothermal storage conditions

文献类型： 外文期刊

第一作者： Fan, Yanwei

作者： Fan, Yanwei;Dong, Ruize;Ji, Zengtao;Shi, Ce;Dong, Ruize;Luo, Yongkang;Tan, Yuqing;Hong, Hui;Fan, Yanwei;Fan, Yanwei;Dong, Ruize;Ji, Zengtao;Shi, Ce;Fan, Yanwei;Dong, Ruize;Ji, Zengtao;Shi, Ce;Ji, Zengtao;Shi, Ce

作者机构：

关键词： Excitation -emission matrices; Parallel factor analysis; Freshness prediction; Radial basis function neural network; Long short-term memory; CNN_LSTM neural network

期刊名称：FOOD CHEMISTRY （ 影响因子：8.5； 五年影响因子：8.2 ）

ISSN： 0308-8146

年卷期： 2024 年 454 卷

页码：

收录情况： SCI

摘要： This study established long short-term memory (LSTM), convolution neural network long short-term memory (CNN_LSTM), and radial basis function neural network (RBFNN) based on optimized excitation-emission matrix (EEM) from fish eye fluid to predict freshness changes of rainbow trout under nonisothermal storage conditions. The method of residual analysis, core consistency diagnostics, and split-half analysis of parallel factor analysis was used to optimize EEM data, and two characteristic components were extracted. LSTM, CNN_LSTM, and RBFNN models based on characteristic components of EEM used to predict the freshness indices. The results demonstrated the relative errors of RBFNN models with an R2 above 0.96 and relative errors less than 10% for Kvalue, total viable counts, and volatile base nitrogen, which were better than those of LSTM and CNN_LSTM models. This study presents a novel approach for predicting the freshness of rainbow trout under nonisothermal storage conditions.

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