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Zero-inflated regressions for modelling microbial low prevalence and sampling performance for foodborne pathogens

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Detalhes bibliográficos
Resumo:Microbial contamination of raw poultry meat could occur because of improper handling at primary production and slaughterhouse levels. Low microbial prevalence data often consists of a high amount of non-detections (zero positives), so a flexible framework is required to characterise the underlying microbial distribution and conduct reliable inferential statistics. Thus, the objective of this work was to evaluate the performance of zeroinflated binomial (ZIB) regression models to describe the effects of sampling site (carcass, thigh, breast, wings) on the measured incidences of Salmonella, Listeria monocytogenes and Staphylococcus aureus on chicken meat. For this aim, a number of fixed- and random-effects models were evaluated and compared, while sampling performance based on mean prevalence estimates was assessed.
Autores principais:Gonzales-Barron, Ursula
Outros Autores:Hernández, Marta; Rodríguez-Lázaro, David; Cadavez, Vasco; Valero, Antonio
Assunto:Zero inflated binomial Poultry meat Markov chain MonteCarlo
Ano:2017
País:Portugal
Tipo de documento:documento de conferência
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
Descrição
Resumo:Microbial contamination of raw poultry meat could occur because of improper handling at primary production and slaughterhouse levels. Low microbial prevalence data often consists of a high amount of non-detections (zero positives), so a flexible framework is required to characterise the underlying microbial distribution and conduct reliable inferential statistics. Thus, the objective of this work was to evaluate the performance of zeroinflated binomial (ZIB) regression models to describe the effects of sampling site (carcass, thigh, breast, wings) on the measured incidences of Salmonella, Listeria monocytogenes and Staphylococcus aureus on chicken meat. For this aim, a number of fixed- and random-effects models were evaluated and compared, while sampling performance based on mean prevalence estimates was assessed.