Ghorbanizamani, Faezeh2025-02-202025-02-202024Ghorbanizamani, F. (2025). A combinatorial approach to chicken meat spoilage detection using color-shifting silver nanoparticles, smartphone imaging, and artificial neural network (ANN). Food Chemistry, 468, 142390.0308-8146https://doi.org/10.1016/j.foodchem.2024.142390https://hdl.handle.net/11454/116101Ensuring 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 mu g/mL and an analytical dynamic range of 10-100 mu g/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 mu g/mL and extending the dynamic range to 0.5-200 mu g/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.en0.1016/j.foodchem.2024.142390info:eu-repo/semantics/closedAccessArtificial intelligenceBiogenic aminesFood freshnessHistamineMeatPoint-of-needSensorArticle468Dec115WOS:0013866020000012-s2.0-8521132615139667235Q1Q1