Storage time prediction of glazed frozen squids during frozen storage at different temperatures based on neural network

• Main Authors: Mingtang Tan, Jinfeng Wang, Peiyun Li, Jing Xie
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-01-01
• Series: International Journal of Food Properties
• Subjects: squid; frozen storage; storage time prediction; backpropagation neural network; long short-term memory neural network
• Online Access: http://dx.doi.org/10.1080/10942912.2020.1825481

In this study, quality changes in water-holding capacity, weight loss, color, texture properties, and total sulfhydrylcontent of glazed frozen squids during frozen storage at −5, −10, −20, −30 and −40°C, were determined. In addition, backpropagation neural network (BP-NN) and long short-term memory neural network (LSTM-NN) model were established to predict storage time of glazed frozen squids, and then these two models were performed with a comparative study. The results showed that the influence on the quality of the squids during frozen storage at different temperatures had significant difference (P < .05), and at the lower storage temperatures, the declined rate of squids’ quality was slower, especially. However, changes in the total SH content of squid stored at −30 and −40°C, were not significant differences in the first 60 days. The squid frozen at −5°C for 80d, reached the end of the shelf life. Both BP-NN and LSTM-NN model ware reliable models for predicting the storage time of glazed frozen squid. Experimental results of the LSTM-NN model provided an improvement in the accuracy of prediction compared with those obtained by using the BP-NN model, in which the mean absolute percentage error (MAPE) was 5.01% that was lower than the results by BP-NN model (7.67%). However, the LSTM-NN model had some shortcomings in terms of training time compared with the BP-NN model. The use of LSTM-NN provides a technique to predict accurately the storage time of glazed frozen squid.