Deep-learning-assisted chemo-responsive alizarin red S-based hydrogel sensor for the rapid freshness sensing of aquatic product.

Abstract

Rapid detection of freshness particularly in aquatic products demands efficient sensing strategies. Here, a novel deep-learning-assisted chemo-responsive alizarin red S-based hydrogel sensing platform was established for rapid freshness assay of aquatic products. Four natural pigments that undergo observable color change to pH were investigated with the presence or absence of water-soluble aquatic proteins, and alizarin red S exhibited ideal responsiveness. The chromogenic hydrogels were fabricated by crosslinking of aldehyde-based sodium alginate and polyethyleneimine-hyaluronic acid, and the pore number of the chromogenic hydrogels were altered by changing contents of aldehyde-based sodium alginate, also resulting enhanced sensitivity in freshness detection. The photosensitive adhesive hydrogel prepared with lipoic acid modified fish skin gelatin made the chromogenic hydrogel more firmly fixed on the surface of aquatic products. The physical and chemical characteristics of the composite hydrogels were analyzed systematically. The composite hydrogels were utilized for detection of fish and shrimp under different storage conditions and excellent sensitivity was displayed. Besides, the deep convolutional neural network model was also conducted to evaluate the detection results. After optimization, established method exhibited high accuracy to 84 % for determining abovementioned freshness levels. This approach provides a promising platform for the advanced non-destructive sensitive detection of freshness detection products.