School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
Food Chem. 2024 Aug 1;448:139078. doi: 10.1016/j.foodchem.2024.139078. Epub 2024 Mar 21.
A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 ∼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 ∼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment.
一种荧光传感器阵列（FSA）与深度学习（DL）技术相结合，从开发到部署，实时监测肉类的新鲜度。该阵列由铜纳米团簇（CuNCs）和荧光染料组成，具有定量和定性检测氨、二甲胺和三甲胺气体的能力，在 5 至 1000 ppm 的范围内检测限低至 131.56 ppb，并且可以在 4°C 下目视监测各种肉类的新鲜度。此外，SqueezeNet 被应用于根据 FSA 图像自动识别肉类的新鲜度，准确率高达 98.17%，并通过使用开放神经网络交换（ONNX）技术进一步部署到个人计算机、移动设备和网站等各种生产环境中。整个肉类新鲜度识别过程仅需 5 至 7 秒。此外，梯度加权类激活映射（Grad-CAM）和一致流形逼近和投影（UMAP）解释算法被用于提高 SqueezeNet 的可解释性和透明度。因此，本研究为 FSA 与 DL 结合在肉类新鲜度智能监测方面的应用提供了新思路。
