Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing, 100083, PR China; Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230027, PR China.
Department of Aquaculture, University of Dubrovnik, 20000, Dubrovnik, Croatia.
Biosens Bioelectron. 2025 Jan 1;267:116770. doi: 10.1016/j.bios.2024.116770. Epub 2024 Sep 10.
Human sensory techniques are inadequate for automating fish quality monitoring and maintaining controlled storage conditions throughout the supply chain. The dynamic monitoring of a single quality index cannot anticipate explicit freshness losses, which remarkably drops consumer acceptability. For the first time, a complete artificial sensory system is designed for the early detection of fish quality prediction. At non-isothermal storages, the rainbow trout quality is monitored by the gas sensors, texturometer, pH meter, camera, and TVB-N analysis. After data preprocessing, correlation analysis identifies the key parameters such as trimethylamine, ammonia, carbon dioxide, hardness, and adhesiveness to input into a back-propagation neural network. Using gas and textural key parameters, around 99 % prediction accuracy is achieved, precisely classifying fresh and spoiled classes. The regression analysis identifies a few gaps due to fewer datasets for model training, which can be reduced using few-shot learning techniques in the future. However, the multiparametric fusion of texture with gases enables early freshness loss detection and shows the capacity to automate the food supply chain completely.
人类的感官技术不足以实现鱼类质量监测的自动化，也无法在整个供应链中保持控制存储条件。单一质量指标的动态监测不能预测明显的新鲜度损失，这极大地降低了消费者的接受度。首次，设计了一个完整的人工感官系统，用于早期检测鱼类质量预测。在非等温储存条件下，通过气体传感器、质构仪、pH 计、摄像头和 TVB-N 分析监测虹鳟鱼的质量。在数据预处理之后，相关分析确定了关键参数，如三甲胺、氨、二氧化碳、硬度和粘性，并将其输入到反向传播神经网络中。使用气体和质地关键参数，可实现约 99%的预测精度，准确地将新鲜和变质的鱼分类。回归分析发现由于模型训练数据集较少，存在一些差距，未来可以使用少样本学习技术来减少这些差距。然而，纹理与气体的多参数融合可以实现早期新鲜度损失的检测，并显示出完全自动化食品供应链的能力。
