食品科学2024，Vol.45Issue(13)：229-238,10.DOI:10.7506/spkx1002-6630-20230701-005
基于CNN-GRU-AE的蓝莓货架期预测模型研究
Convolutional Neural Network-Gated Recurrent Unit-Attention Based Model for Blueberry Shelf Life Prediction
摘要
Abstract
In order to investigate the quality changes and shelf life of blueberries stored in different temperature,21 quality indexes,including color parameters,mass loss rate,spoilage rate and texture parameters,were measured on"Freedom"blueberries at three storage temperatures(0,4 and 25 ℃).Using five machine learning algorithms with a self-contained function of feature selection,seven key features affecting the shelf life were selected as input variables to construct a shelf life prediction model using gated recurrent unit(GRU)alone or in combination with convolutional neural network(CNN)and/or attention(AE)mechanism.The results showed that compared with the GRU model,the mean absolute error(MAE),mean square error(MSE)and mean absolute percentage error(MAPE)of the CNN-GRU-AE model decreased by 75.83％,91.46％,61.58％,respectively,and the coefficient and determination increased by 2.25％,indicating significantly improved accuracy of shelf-life prediction.This study provides theoretical support for the shelf life prediction of blueberries at different storage temperatures.关键词
蓝莓/货架期预测/卷积神经网络/门控循环单元/注意力机制Key words
blueberry/shelf life prediction/convolutional neural network/gated recurrent unit/attention mechanism分类
轻工纺织引用本文复制引用
张润泽,冯国红,付晟宏,王宏恩,高珊,朱玉杰,刘中深..基于CNN-GRU-AE的蓝莓货架期预测模型研究[J].食品科学,2024,45(13):229-238,10.基金项目
国家自然科学基金面上项目(32071685) （32071685）
黑龙江省自然科学基金项目(LH2020C050) （LH2020C050）
