Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/347465
Type: Artigo
Title: Evaluation of extra virgin olive oil stability by artificial neural network
Author: Silva, Simone Faria
Anjos, Carlos Alberto Rodrigues
Cavalcanti, Rodrigo Nunes
Celeghini, Renata Maria dos Santos
Abstract: The 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∗Cab∗hab∗ color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power
Subject: Compostos bioativos
Country: Países Baixos
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.foodchem.2015.01.100
Address: https://www.sciencedirect.com/science/article/pii/S0308814615001156
Date Issue: 2015
Appears in Collections:FEA - Artigos e Outros Documentos

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