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Improving microbial growth prediction by product unit neural networks

dc.contributor.authorHervas Martínez, César
dc.contributor.authorGarcía Gimeno, R.M.
dc.contributor.authorMartínez Estudillo, Alfonso Carlos 
dc.contributor.authorMartínez Estudillo, Francisco José 
dc.contributor.authorZurera Cosano, G.
dc.date.accessioned2019-02-04T15:19:12Z
dc.date.available2019-02-04T15:19:12Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/20.500.12412/1016
dc.description.abstractThis article presents a new approach to the Artificial Neural Networks (ANN) modeling of bacterial growth; using Neural Network models based on Product Units (PUNN) instead of on sigmoidal units (multilayer perceptron type [MLP]) of kinetic parameters (lag‐time, growth rate, and maximum population density) of Leuconostoc mesenteroides and those factors affecting their growth such as storage temperature, pH, NaCl, and NaNO2 concentrations under anaerobic conditions.es
dc.language.isoenges
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleImproving microbial growth prediction by product unit neural networkses
dc.typearticlees
dc.identifier.doi10.1111/j.1365-2621.2006.tb08904.x
dc.issue.number2es
dc.journal.titleJournal Of Food Sciencees
dc.page.initial31es
dc.page.final38es
dc.rights.accessRightsopenAccesses
dc.subject.keywordartificial neural networkses
dc.subject.keywordproduct Unitses
dc.subject.keywordgrowth modeles
dc.subject.keywordleuconostoc mesenteroideses
dc.subject.keywordspoilage bacteriaes
dc.volume.number71es


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional