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dc.date.accessioned 2019-11-08T14:08:47Z
dc.date.available 2019-11-08T14:08:47Z
dc.date.issued 2013
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/85211
dc.description.abstract In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm. en
dc.format.extent 411-416 es
dc.language en es
dc.subject Artificial neural networks es
dc.subject Dried apple es
dc.subject Genetic algorithm es
dc.subject Process optimization es
dc.subject Quality attributes es
dc.title An artificial neural network model for prediction of quality characteristics of apples during convective dehydration en
dc.type Articulo es
sedici.identifier.other doi:10.1590/S0101-20612013005000064 es
sedici.identifier.other eid:2-s2.0-84885640177 es
sedici.identifier.issn 1678-457X es
sedici.creator.person Di Scala, Karina es
sedici.creator.person Meschino, Gustavo es
sedici.creator.person Vega-Gálvez, Antonio es
sedici.creator.person Lemus-Mondaca, Roberto es
sedici.creator.person Roura, Sara es
sedici.creator.person Mascheroni, Rodolfo Horacio es
sedici.subject.materias Ingeniería es
sedici.description.fulltext true es
mods.originInfo.place Centro de Investigación y Desarrollo en Criotecnología de Alimentos es
mods.originInfo.place Facultad de Ingeniería es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Food Science and Technology es
sedici.relation.journalVolumeAndIssue vol. 33, no. 3 es
sedici.rights.sherpa * Color: gray * Pre-print del autor: unknown * Post-print del autor: unknown * Versión de editor/PDF:unknown * Condiciones: >>This publisher's policies have not been checked by Color. >>DOAJ says it is an open access journal, but this may only mean that it is freely available to read. >>Most open access publishers also permit self-archiving and re-use, but some do not. >>Do not assume that self-archiving is allowed, unless it is published under a Creative Commons license. >>Please contact the publisher for further information if necessary >>Email romeo@jisc.ac.uk if you wish to suggest adding this publisher properly to Color. * Link a Sherpa: http://sherpa.ac.uk/romeo/issn/0101-2061/es/


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)