A combinatorial approach to chicken meat spoilage detection using color-shifting silver nanoparticles, smartphone imaging, and artificial neural network (ANN).

Ensuring food freshness is crucial for public health. Biogenic amines (like histamine) are reliable spoilage indicators in protein-rich foods such as meat. This study presents a label-free colorimetric sensor using green-colored silver nanoparticles (AgNPs) functionalized with carboxylated polyvinylpyrrolidone (PVP-COOH) for sensitive BA detection. After optimizing pH, time, and temperature, the modified AgNPs achieved a detection limit (LOD) of 0.21 μg/mL and an analytical dynamic range of 10-100 μg/mL for histamine. Smartphone imaging was employed to capture colorimetric changes, and the extracted data were used to train an artificial neural network (ANN), enhancing the LOD to 0.09 μg/mL and extending the dynamic range to 0.5-200 μg/mL. The sensor was validated with real food samples, successfully monitoring histamine levels in chicken meat over three days, detecting spoilage-related changes with high sensitivity. This integrative approach combining AgNPs, smartphone imaging, and AI offers a powerful tool for advanced food freshness monitoring.