基于卷积神经网络的熟化鱼肉纤维程度评价方法研究

Evaluation of Fiber Degree for Cooked Fish Muscle Based on the Convolutional Neural Network

为了打破熟化鱼肉纤维口感无系统性评价方法的局面,基于卷积神经网络构建熟化鱼肉纤维程度的评价方法。通过对不同纤维程度的熟化鱼肉样品进行微观图片采集,建立数据集。数据集按8∶2的比例随机分为训练集和测试集。构建的模型在训练集上训练,然后用测试集评估其识别性能。结果表明,在网络深度的选择时,34层的ResNet模型在收敛速度和准确率上均胜出;4种不同深度的ResNet模型在最佳识别准确率上都优于AlexNet、VGG-16和GoogLeNet模型;ResNet-34的准确性、精度、灵敏度、特异性和AUC的平均值分别为96.94%,91.26%,91.00%,98.13%和99.19%,表明基于ResNet-34模型搭建的评价方法能够客观、准确地识别熟化鱼肉纤维程度。

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.