Preparation of multifunctional hydrogels based on co-pigment-polysaccharide complexes and establishment of a machine learning monitoring platform.

Economic loss due to fish spoilage exceeds 25 billion euros every year. Accurate and real-time monitoring of the freshness of fish can effectively cut down economic loss and food wastage. In this study, a dual-functional hydrogel based on sodium alginate-co-pigment complex with volatile antibacterial and intelligent indication was prepared and characterized. The characterization results indicated that the sodium alginate-co-pigment complex successfully improved the stability and color development ability of blueberry anthocyanins and bilberry anthocyanins at different temperatures and pH. The double cross-linking network inside the hydrogel conferred it with excellent mechanical properties. During rainbow trout storage, the hydrogel indicated a color difference of 73.55 on the last day and successfully extended the shelf-life of rainbow trout by 2 days (4 °C). Additionally, four dual-channel monitoring models were constructed using machine learning. The validation error of the genetic algorithm back propagation model (GA-BP) was only 5.6e-3, indicating that GA-BP can accurately monitor the freshness of rainbow trout. The rainbow trout real-time monitoring platform built based on GA-BP model can monitor the freshness of rainbow trout in real time through the images uploaded by users. The results of this study have broad applicability in the food industry, environmental conservation, and economic sustainability.