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Home > Archive>Volume 23, Issue 9, 2023 >252-260. DOI:10.16429/j.1009-7848.2023.09.026
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Evaluation of Fiber Degree for Cooked Fish Muscle Based on the Convolutional Neural Network
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(1.State Key Laboratory of Food Science and Resources， Jiangnan University， Wuxi 214122， Jiangsu;2.Key Laboratory of Refrigeration and Conditioning Aquatic Products Processing, Ministry of Agriculture and Rural Affairs， Xiamen 361022， Fujian;3.School of Food Science and Technology, Jiangnan University, Wuxi 214122， Jiangsu;4.Fujian Provincial Key Laboratory of Refrigeration and Conditioning Aquatic Products Processing, Xiamen 361022， Fujian;5.Anjoy Foods Group Co.， Ltd.， Xiamen 361022， Fujian

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    Abstract:

    An evaluation method for cooked fish fiber degree was constructed based on the convolutional neural network in order to break the situation that there was no systematic evaluation method for cooked fish fiber taste. A dataset was established by collecting microscopic images of cooked fish samples with different fiber degrees. The dataset was randomly divided into training dataset and testing dataset in the ratio of 8∶2. The constructed models were trained on the training dataset and their recognition performance were evaluated by the testing dataset. The result showed that the 34-layer ResNet model win in convergence speed and accuracy when choosing network depth. Four ResNet models with different depths were better than AlexNet， VGG-16 and GoogLeNet models in the best recognition accuracy. The average accuracy， precision， sensitivity and specificity and AUC of ResNet-34 were 96.94%， 91.26%， 91.00%， 98.13% and 99.19%， respectively， which proved that the evaluation method based on ResNet-34 model could objectively and accurately identify the degree of cooked fish fiber.

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  • Received:September 02,2022
  • Online: November 22,2023
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