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

Journal: Food chemistry
PMID: 38810453

Abstract

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 R above 0.96 and relative errors less than 10% for K-value, 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.

Authors

  • Yanwei Fan
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China; National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China.
  • Ruize Dong
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China.
  • Yongkang Luo
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
  • Yuqing Tan
    Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
  • Hui Hong
    School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
  • Zengtao Ji
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
  • Ce Shi
    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China.

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