CNN-integrated ratiometric fluorescent tag for rapid monitoring of seafood freshness at refrigerated temperatures

Given the perishable nature of seafood, accurate and rapid freshness monitoring is essential for ensuring food safety and public health. In this study, a ratiometric fluorescent tag was proposed for detecting ammonia to assess seafood freshness, using D-penicillamine-capped silver/copper nanoclusters (DPA-AgCuNCs) and fluorescein isothiocyanate (FITC) as dual-emission probes. The synthesized DPA-AgCuNCs with aggregation-induced emission (AIE) properties exhibited high sensitivity toward ammonia, with a limit of detection of 2.95 ppm (3σ/s). By integrating orange-emitting DPA-AgCuNCs with green-emissive FITC, the fabricated ratiometric fluorescent tag exhibited a distinct orange-to-green fluorescence transition with increasing ammonia concentration, enabling direct visual assessment of freshness. A fluorescence colour card was further established for rapid preliminary differentiation among fresh, less fresh, and spoiled samples. Convolutional neural network (CNN)-assisted image analysis was introduced as a complementary approach to improve the objectivity and consistency of freshness classification, achieving precision values of 95.24%, 100%, and 100% for fresh, less fresh, and spoiled shrimp, respectively. For on-site monitoring, the CNN was integrated into a smartphone application (FreshSense), constructing a simple platform for rapid monitoring of seafood freshness. This proof-of-concept system is rapid, on-site, and non-destructive, demonstrating its potential for practical screening in food quality and safety.