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.
Keywords: Aerogel; Color-rendering ability; Dual-channel; High stability; Machine learning; Monitoring.
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