Abstract：
Global seafood consumption is estimated at 156 million tons annually, with an economic loss of >25 billion euros annually due to marine fish spoilage. In contrast to traditional smart packaging which can only roughly estimate food freshness, an intelligent platform integrating machine learning and smart aerogel can accurately predict remaining shelf life in food products, reducing economic losses and food waste. In this study, we prepared aerogels based on anthocyanin complexes that exhibited excellent environmental responsiveness, high porosity, high color-rendering properties, high biocompatibility, high stability, and irreversibility. The aerogel showed excellent indication properties for rainbow trout and proved suitable for fish storage environments. Among the four machine learning models, the radial basis function neural network and backpropagation network optimized by genetic algorithm demonstrated excellent monitoring performance. Also, the two-channel dataset provided more comprehensive information and superior descriptive capability. The three-layer structure of the monitoring platform provided a new paradigm for intelligent and sophisticated food packaging. The results of the study might be of great significance to the food industry and sustainable development.
摘要：
全球海产品消费量估计为每年1.56亿吨，由于海洋鱼类变质，每年造成的经济损失>250亿欧元。与传统的智能包装相比，传统的智能包装只能粗略地估计食物的新鲜度，集成机器学习和智能气凝胶的智能平台可以准确预测食品的剩余保质期，减少经济损失和食物浪费。在这项研究中,我们制备了基于花色苷复合物的气凝胶，表现出优异的环境响应性，高孔隙率,高显色性，高生物相容性，高稳定性,和不可逆性。气凝胶对虹鳟鱼表现出优异的指示特性，并被证明适用于鱼类储存环境。在四种机器学习模型中，遗传算法优化后的径向基函数神经网络和反向传播网络具有良好的监测性能。此外，双通道数据集提供了更全面的信息和优越的描述能力。监测平台的三层结构为智能和复杂的食品包装提供了新的范例。研究结果对食品工业和可持续发展具有重要意义。
