State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China; Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, 214122 Wuxi, Jiangsu, China.
State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China; China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, 214122 Wuxi, Jiangsu, China.
Food Res Int. 2023 Feb;164:112420. doi: 10.1016/j.foodres.2022.112420. Epub 2022 Dec 28.
Vegetable sauerkraut is a traditional fermented food. Due to oxidation reactions that occur during storage, the quality and flavor in different periods will change. In this study, the quality evaluation and flavor characteristics of 13 groups of vegetable sauerkraut samples with different storage time were analyzed by using physical and chemical parameters combined with electronic nose. Photographs of samples of various periods were collected, and a convolutional neural network (CNN) framework was established. The relationship between total phenol oxidative decomposition and flavor compounds was linearly negatively correlated. The vegetable sauerkraut during storage can be divided into three categories (full acceptance period, acceptance period and unacceptance period) by principal component analysis and Fisher discriminant analysis. The CNN parameters were fine-tuned based on the classification results, and its output results can reflect the quality changes and flavor characteristics of the samples, and have better fitting, prediction capabilities. After 50 epochs of the model, the accuracy of three sets of data namely training set, validation set and test set recorded 94%, 85% and 93%, respectively. In addition, the accuracy of CNN in identifying different quality sauerkraut was 95.30%. It is proved that the convolutional neural network has excellent performance in predicting the quality of Szechuan Sauerkraut with high reliability.
泡菜是一种传统的发酵食品。由于在贮藏过程中发生氧化反应，不同时期的品质和风味会发生变化。本研究采用物理化学参数结合电子鼻分析了 13 组不同贮藏时间的泡菜样品的品质评价和风味特征。采集了不同时期样品的照片，建立了卷积神经网络（CNN）框架。总酚氧化分解与风味化合物之间呈线性负相关。通过主成分分析和 Fisher 判别分析，将贮藏过程中的泡菜分为完全可接受期、可接受期和不可接受期三个类别。根据分类结果对 CNN 参数进行微调，其输出结果能够反映样品的质量变化和风味特征，具有更好的拟合和预测能力。在模型经过 50 个周期后，训练集、验证集和测试集的准确率分别记录为 94%、85%和 93%。此外，CNN 识别不同质量泡菜的准确率为 95.30%。证明了卷积神经网络在预测四川泡菜质量方面具有优异的性能，可靠性高。
