Evaluation Of Extra Virgin Olive Oil Stability By Artificial Neural Network

dc.creatorSilva
dc.creatorSimone Faria; Rodrigues Anjos
dc.creatorCarlos Alberto; Cavalcanti
dc.creatorRodrigo Nunes; dos Santos Celeghini
dc.creatorRenata Maria
dc.date2015-Jul
dc.date2016-06-07T13:16:26Z
dc.date2016-06-07T13:16:26Z
dc.date.accessioned2018-03-29T01:37:03Z
dc.date.available2018-03-29T01:37:03Z
dc.identifier
dc.identifierEvaluation Of Extra Virgin Olive Oil Stability By Artificial Neural Network. Elsevier Sci Ltd, v. 179, p. 35-43 Jul-2015.
dc.identifier0308-8146
dc.identifierWOS:000352926400005
dc.identifier10.1016/j.foodchem.2015.01.100
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0308814615001156
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/242085
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1305783
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionThe stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6 months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270 nm, chlorophyll, L*C*h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L*C-ab*h*(ab) color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power. (C) 2015 Elsevier Ltd. All rights reserved.
dc.description179
dc.description
dc.description
dc.description35
dc.description43
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFAPESP [2009/16849-0]
dc.descriptionCNPq [130689/2009-2]
dc.description
dc.description
dc.description
dc.languageen
dc.publisherELSEVIER SCI LTD
dc.publisher
dc.publisherOXFORD
dc.relationFOOD CHEMISTRY
dc.rightsembargo
dc.sourceWOS
dc.subjectSupervised Pattern-recognition
dc.subjectMill Waste-water
dc.subjectMultivariate-analysis
dc.subjectPhenolic-compounds
dc.subjectElectronic Tongue
dc.subjectApple Beverages
dc.subjectShelf-life
dc.subjectClassification
dc.subjectQuality
dc.subjectSystem
dc.titleEvaluation Of Extra Virgin Olive Oil Stability By Artificial Neural Network
dc.typeArtículos de revistas


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