State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, PR China.
Key Laboratory of Molecular Recognition and Sensing, College of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing 314001, PR China.
Food Chem. 2024 Jul 30;447:138931. doi: 10.1016/j.foodchem.2024.138931. Epub 2024 Mar 4.
Gas sensors containing indicators have been widely used in meat freshness testing. However, concerns about the toxicity of indicators have prevented their commercialization. Here, we prepared three fluorescent sensors by complexing each flavonoid (fisetin, puerarin, daidzein) with a flexible film, forming a fluorescent sensor array. The fluorescent sensor array was used as a freshness indication label for packaged meat. Then, the images of the indication labels on the packaged meat under different freshness levels were collected by smartphones. A deep convolutional neural network (DCNN) model was built using the collected indicator label images and freshness labels as the dataset. Finally, the model was used to detect the freshness of meat samples, and the overall accuracy of the prediction model was as high as 97.1%. Unlike the TVB-N measurement, this method provides a nondestructive, real-time measurement of meat freshness.
含有指示剂的气体传感器已被广泛应用于肉类新鲜度检测。然而，指示剂的毒性问题一直阻碍着它们的商业化。在这里，我们通过将三种黄酮类化合物（非瑟酮、葛根素、大豆苷元）分别与柔性膜复合，制备了三种荧光传感器，形成了荧光传感器阵列。该荧光传感器阵列被用作包装肉的新鲜度指示标签。然后，通过智能手机收集不同新鲜度水平下包装肉上指示标签的图像。使用收集到的指示标签图像和新鲜度标签作为数据集，构建了一个深度卷积神经网络（DCNN）模型。最后，该模型用于检测肉样的新鲜度，预测模型的整体准确率高达 97.1%。与TVB-N 测量方法不同，该方法提供了一种无损、实时的肉类新鲜度测量方法。
