Convolutional Neural Network-Gated Recurrent Unit-Attention basedModel for Blueberry Shelf Life Prediction

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.

Keywords

• blueberry; shelf life prediction; convolutional neural network; gated recurrent unit; attention mechanism