Probability models for growth and aflatoxin B1 production as affected by intraspecies variability in Aspergillus flavus

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dc.contributor.authorAldars García, Laila
dc.contributor.authorBerman, María
dc.contributor.authorOrtiz Solà, Jordi
dc.contributor.authorRamos Girona, Antonio J.
dc.contributor.authorMarín Sillué, Sònia
dc.date.accessioned2017-12-19T09:48:47Z
dc.date.available2018-12-01T23:28:59Z
dc.date.issued2018
dc.date.updated2017-12-19T09:48:50Z
dc.description.abstractThe probability of growth and aflatoxin B1 (AFB1) production of 20 isolates of Aspergillus flavus were studied using a full factorial design with eight water activity levels (0.84e0.98 aw) and six temperature levels (15e40 C). Binary data obtained from growth studies were modelled using linear logistic regression analysis as a function of temperature, water activity and time for each isolate. In parallel, AFB1 was extracted at different times from newly formed colonies (up to 20 mm in diameter). Although a total of 950 AFB1 values over time for all conditions studied were recorded, they were not considered to be enough to build probability models over time, and therefore, only models at 30 days were built. The confidence intervals of the regression coefficients of the probability of growth models showed some differences among the 20 growth models. Further, to assess the growth/no growth and AFB1/no- AFB1 production boundaries, 0.05 and 0.5 probabilities were plotted at 30 days for all of the isolates. The boundaries for growth and AFB1 showed that, in general, the conditions for growth were wider than those for AFB1 production. The probability of growth and AFB1 production seemed to be less variable among isolates than AFB1 accumulation. Apart from the AFB1 production probability models, using growth probability models for AFB1 probability predictions could be, although conservative, a suitable alternative. Predictive mycology should include a number of isolates to generate data to build predictive models and take into account the genetic diversity of the species and thus make predictions as similar as possible to real fungal food contamination.
dc.description.sponsorshipThe authors are grateful to the Agència de Gestió d’Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (AGAUR, grant 2014FI_B 00045) and to the Spanish Ministry of Economy and Competitiveness (MINECO, Project AGL2014-55379-P) for funding this work.
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1016/j.fm.2017.11.015
dc.identifier.idgrec026343
dc.identifier.issn0740-0020
dc.identifier.urihttp://hdl.handle.net/10459.1/60757
dc.language.isoeng
dc.publisherElsevier
dc.relationMINECO/PN2013-2016/AGL2014-55379-P
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1016/j.fm.2017.11.015
dc.relation.ispartofFood Microbiology, 2018, vol. 72, p. 166-175
dc.rightscc-by-nc-nd (c) Elsevier, 2017
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntraspecies variability
dc.subjectPredictive mycology
dc.subjectProbability models
dc.subjectAspergillus
dc.titleProbability models for growth and aflatoxin B1 production as affected by intraspecies variability in Aspergillus flavus
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
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