An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information

• Published in: Foods
• Main Authors: Tao Lu, Fanqianhui Yu, Baokun Han, Jingying Guo, Kunhua Liu, Shuai He
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: oyster; convolutional neural network; freshness prediction; feature visualization; strongest activations; Chemical technology; TP1-1185; Pacific; Crassostrea gigas; Pacific oyster
• Online Access: https://doi.org/10.3390/foods12193616; https://doaj.org/article/c9a545516c5449a3a5b04faf15508f86

To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an “MDA-SH-storage days” polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.72% and 94.06%, respectively. Next, the outputs of the two models were used as the inputs to “MDA-SH-storage days” model, which ultimately succeeded in predicting the corresponding MDA content, SH content and storage day for an oyster image within 0.03 ms. Furthermore, the interpretability of the two models for oyster meat image were also investigated by feature visualization and strongest activations techniques. Thus, this study brings new thoughts on oyster freshness prediction from the perspective of computer vision and artificial intelligence.