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An alternative framework to conduct inferential statistics for low microbial counts in foods: the poisson-gamma regression

dc.contributor.authorGonzales-Barron, Ursula
dc.contributor.authorCadavez, Vasco
dc.contributor.authorButler, Francis
dc.date.accessioned2018-05-09T09:56:03Z
dc.date.available2018-05-09T09:56:03Z
dc.date.issued2013
dc.description.abstractThe objective of this article was to compare four Poisson-gamma regression models to assess the effect of chilling on the concentration of coliforms from beef carcasses. A total of 600 carcasses were sampled before and after chilling at eight large Irish abattoirs, and the total coliforms were determined. With a coded variable (pre-chill/post-chill) as treatment, and extracting the variability of batches nested in abattoirs, random-effects models confirmed that chilling had a decreasing effect on the overall recovery of coliforms. Furthermore, the expected coliforms concentrations on pre-chill and post-chill carcasses were estimated on a CFU/cm2 scale, as well as their between-batch variability. This study introduced an alternative conceptual framework that can find interesting applications in stochastic risk assessment and in the design of more efficient sampling plans.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGonzales-Barron, Ursula A.; Cadavez, Vasco; Butler, Francis (2013). An alternative framework to conduct inferential statistics for low microbial counts in foods: the poisson-gamma regression. In Proceedings of International Conference on Food and Biosystems Engineering - FaBE 2013. Skiathos Island, Greece. Vol. 1, p. 522-531. ISBN 978-960-9510-09-7pt_PT
dc.identifier.isbn978-960-9510-09-7
dc.identifier.urihttp://hdl.handle.net/10198/17612
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNegative binomialpt_PT
dc.subjectChillingpt_PT
dc.subjectColiformspt_PT
dc.subjectCarcasspt_PT
dc.titleAn alternative framework to conduct inferential statistics for low microbial counts in foods: the poisson-gamma regressionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceSkiathos Island, GREECEpt_PT
oaire.citation.endPage531pt_PT
oaire.citation.startPage522pt_PT
oaire.citation.titleProceedings of FaBE 2013 International Conferences on Food and Biosystems Engineeringpt_PT
person.familyNameGonzales-Barron
person.familyNameCadavez
person.givenNameUrsula
person.givenNameVasco
person.identifierR-000-HDG
person.identifier.ciencia-id0813-C319-B62A
person.identifier.ciencia-id441B-01AB-A12E
person.identifier.orcid0000-0002-8462-9775
person.identifier.orcid0000-0002-3077-7414
person.identifier.ridA-3958-2010
person.identifier.scopus-author-id9435483700
person.identifier.scopus-author-id9039121900
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication17c6b98f-4fb5-41d3-839a-6f77ec70021a
relation.isAuthorOfPublication57b410e9-f6b7-42ff-ab3d-b526278715eb
relation.isAuthorOfPublication.latestForDiscovery57b410e9-f6b7-42ff-ab3d-b526278715eb

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