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
作者： Fan, Yanwei 1 ; Dong, Ruize 1 ; Luo, Yongkang 2 ; Tan, Yuqing 2 ; Hong, Hui 2 ; Ji, Zengtao 1 ; Shi, Ce 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
2.China Agr Univ, Coll Food Sci & Nutr Engn, Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China
3.Shenyang Agr Univ, Coll Informat & Elect Engn, Shenyang 110866, Peoples R China
4.Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
5.Beijing Acad Agr & Forestry Sci, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China
6.Minist Agr & Rural Affairs, Key Lab Cold Chain Logist Technol Agroprod, Beijing 100097, Peoples R China
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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