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

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dc.authorid0000-0001-8541-0540
dc.contributor.authorGhorbanizamani, Faezeh
dc.date.accessioned2025-02-20T12:13:28Z
dc.date.available2025-02-20T12:13:28Z
dc.date.issued2024
dc.departmentEge Üniversitesi, Fen Fakültesi, Biyokimya Bölümü
dc.description.abstractEnsuring 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.
dc.identifier.citationGhorbanizamani, 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.
dc.identifier.doi0.1016/j.foodchem.2024.142390
dc.identifier.endpage15
dc.identifier.issn0308-8146
dc.identifier.issueDec
dc.identifier.pmid39667235
dc.identifier.scopus2-s2.0-85211326151
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1016/j.foodchem.2024.142390
dc.identifier.urihttps://hdl.handle.net/11454/116101
dc.identifier.volume468
dc.identifier.wosWOS:001386602000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorGhorbanizamani, Faezeh
dc.institutionauthorid0000-0001-8541-0540
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofFood Chemistry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial intelligence
dc.subjectBiogenic amines
dc.subjectFood freshness
dc.subjectHistamine
dc.subjectMeat
dc.subjectPoint-of-need
dc.subjectSensor
dc.titleA combinatorial approach to chicken meat spoilage detection using color-shifting silver nanoparticles, smartphone imaging, and artificial neural network (ANN)
dc.typeArticle

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