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

Tao Lu , Fanqianhui Yu, Baokun Han , Jingying Guo, Kunhua Liu, Shuai He

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Abstract

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.
Original languageEnglish
Article number3616
Number of pages12
JournalFoods
Volume12
Issue number19
Early online date28 Sept 2023
DOIs
Publication statusE-pub ahead of print - 28 Sept 2023

Bibliographical note

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords

  • convolutional neural network
  • feature visualization
  • freshness prediction
  • oyster
  • strongest activations

ASJC Scopus subject areas

  • Health(social science)
  • Food Science
  • Health Professions (miscellaneous)
  • Plant Science
  • Microbiology

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