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Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks

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Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks

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dc.contributor.author Mateo Jiménez, Fernando es_ES
dc.contributor.author Gadea Gironés, Rafael es_ES
dc.contributor.author Medina, A. es_ES
dc.contributor.author Mateo, R. es_ES
dc.contributor.author Jiménez, M. es_ES
dc.date.accessioned 2016-03-08T10:23:31Z
dc.date.available 2016-03-08T10:23:31Z
dc.date.issued 2009-09
dc.identifier.issn 1364-5072
dc.identifier.uri http://hdl.handle.net/10251/61542
dc.description.abstract Aims: To study the ability of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict ochratoxin A (OTA) concentration over time in grape-based cultures of Aspergillus carbonarius under different conditions of temperature, water activity (a(w)) and sub-inhibitory doses of the fungicide carbendazim. Methods and Results: A strain of A. carbonarius was cultured in a red grape juice-based medium. The input variables to the network were temperature (20-28 degrees C), a(w) (0 center dot 94-0 center dot 98), carbendazim level (0-450 ng ml(-1)) and time (3-15 days after the lag phase). The output of the ANNs was OTA level determined by liquid chromatography. Three algorithms were comparatively tested for MLP. The lowest error was obtained by MLP without validation. Performance decreased when hold-out validation was accomplished but the risk of over-fitting is also lower. The best MLP architecture was determined. RBFNs provided similar performances but a substantially higher number of hidden nodes were needed. Conclusions: ANNs are useful to predict OTA level in grape juice cultures of A. carbonarius over a range of a(w), temperature and carbendazim doses. Significance and Impact of the Study: This is a pioneering study on the application of ANNs to forecast OTA accumulation in food based substrates. These models can be similarly applied to other mycotoxins and fungal species. es_ES
dc.description.sponsorship This work was supported by the Spanish 'Ministerio de Educacion y Ciencia' (projects AGL-2004-07549-C05-02 and AGL2007-66416-C05-01 and a research grant) and the Valencian Government 'Conselleria de Empresa, Universitat i Ciencia' (project GV04B-111 and ACOMP/2007/155 and a research grant). en_EN
dc.language Inglés es_ES
dc.publisher Wiley es_ES
dc.relation Spanish Ministerio de Educación y Ciencia AGL-2004-07549-C05-02 es_ES
dc.relation Spanish Ministerio de Educación y Ciencia AGL2007-66416-C05-01 es_ES
dc.relation Valencian Government Conselleria de Empresa, Universitat i Ciencia GV04B-111 es_ES
dc.relation Valencian Government Conselleria de Empresa, Universitat i Ciencia ACOMP/2007/155 es_ES
dc.relation.ispartof Journal of Applied Microbiology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Aspergillus carbonarius es_ES
dc.subject Grape-based products es_ES
dc.subject Mycotoxigenic fungi es_ES
dc.subject Mycotoxins es_ES
dc.subject Neural networks es_ES
dc.subject Ochratoxin A es_ES
dc.subject Predictive mycology es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1111/j.1365-2672.2009.04264.x
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Mateo Jiménez, F.; Gadea Gironés, R.; Medina, A.; Mateo, R.; Jiménez, M. (2009). Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks. Journal of Applied Microbiology. 107(3):915-927. doi:10.1111/j.1365-2672.2009.04264.x es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1111/j.1365-2672.2009.04264.x es_ES
dc.description.upvformatpinicio 915 es_ES
dc.description.upvformatpfin 927 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 107 es_ES
dc.description.issue 3 es_ES
dc.relation.senia 39546 es_ES
dc.identifier.eissn 1365-2672
dc.identifier.pmid 19486411


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