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Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics

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Food Quality and Safety 2021年第1期5卷 31-41页

作者： llaria Lanza daniele conficoni Stefania Balzan Marco Cullere Luca Fasolato Lorenzo Servap Barbara Contiero Angela Trocino Giorgio Marchesini Gerolamo Xiccato Enrico Novelli Severino Segato Department of Animal Medicine Productions and HealthUniversity of PadovaLegnaroItaly Department of Comparative Biomedicine and Food Science University of PadovaLegnaroItaly Department of Agronomy FoodNatural ResourcesAnimals and EnvironmentUniversity of PadovaLegnaroItaly

Objectives:Near-infra red(NIR)spectroscopy is a rapid technique able to assess meat quality even if its capability to determine the shelf life of chicken fresh cuts is still debated,especially for portable *** aim of the study was to compare bench-top and portable NIR instruments in discriminating between four chicken breast refrigeration times(RT),coupled with multivariate classifier *** and Methods:Ninety-six samples were analysed by both NIR tools at 2,6,10 and 14 days post *** data were subsequently submitted to partial least squares discriminant analysis(PLS-DA)and canonical discriminant analysis(CDA).The latter was preceded by double feature selection based on Boruta and Stepwise ***:PLS-DA sorted moderate separation of RT theses,while shelf life assessment was more accurate on application of ***-top tool had better performance than portable one,probably because it captured more informative spectral data as shown by the variable importance in projection(VIP)and restricted pool of Stepwise-CDA predictive scores(SPS).Conclusions:NIR tools coupled with a multivariate model provide deep insight into the physicochemical processes occurring during *** showed reliable effectiveness to recognise a 7-day shelf life threshold of breasts,suitable for routine at-line application for screening of meat quality.

关键词： shelf life chicken breast Near-infrared(NIR)spectroscopy bench-top NIR portable NIR canonical discriminant analysis

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