Application of artificial neural networks in the prediction of sugarcane juice Pol

dc.contributor.authorCoelho, Anderson P. [UNESP]
dc.contributor.authorBettiol, Joao V. T. [UNESP]
dc.contributor.authorDalri, Alexandre B. [UNESP]
dc.contributor.authorFischer Filho, Joao A. [UNESP]
dc.contributor.authorFaria, Rogerio T. de [UNESP]
dc.contributor.authorPalaretti, Luiz F. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-04T11:56:52Z
dc.date.available2019-10-04T11:56:52Z
dc.date.issued2019-01-01
dc.description.abstractInnovative techniques that seek to minimize the costs of production and the laboriousness of certain operations are one of the great challenges in the sugar-energy sector nowadays. Thus, the objective of the present study was to estimate the Pol values of sugarcane juice as a function of degrees Brix and wet cake weight (WCW) using artificial neural network (ANN) modeling. A database was organized consisting of 204 technological analyses from a field experiment with 15 treatments and 2 years of evaluation. 75% of the data were used for the calibration of the model and 25% for its validation. Multilayer Perceptron ANNs were used for calibration and validation of the data. Before calibration, the variables were normalized. The training algorithm used was backpropagation and the activation function was the sigmoid. The ANNs were established with two hidden layers and the number of neurons ranging from 4 to 20 in each. The 15 ANNs with the lowest root mean square errors were randomly presented by the software, among which 6 were chosen to verify the accuracy. The ANNs had a high accuracy in the estimation of sugarcane juice Pol, both in the calibration phase (R-2 = 0.948, RMSE = 0.36%) and in the validation (R-2 = 0.878, RMSE = 0.41%), and can replace the standard method of analysis. Simpler networks can be trained to have the same accuracy as more complex networks.en
dc.description.affiliationUniv Estadual Paulista, Fac Ciencias Agr & Vet, Jaboticabal, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Fac Ciencias Agr & Vet, Jaboticabal, SP, Brazil
dc.format.extent9-15
dc.identifierhttp://dx.doi.org/10.1590/1807-1929/agriambi.v23n1p9-15
dc.identifier.citationRevista Brasileira De Engenharia Agricola E Ambiental. Campina Grande Pb: Univ Federal Campina Grande, v. 23, n. 1, p. 9-15, 2019.
dc.identifier.doi10.1590/1807-1929/agriambi.v23n1p9-15
dc.identifier.fileS1415-43662019000100009.pdf
dc.identifier.issn1807-1929
dc.identifier.scieloS1415-43662019000100009
dc.identifier.urihttp://hdl.handle.net/11449/184346
dc.identifier.wosWOS:000458643800002
dc.language.isoeng
dc.publisherUniv Federal Campina Grande
dc.relation.ispartofRevista Brasileira De Engenharia Agricola E Ambiental
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjecttrs
dc.subjectBrix
dc.subjectsucrose
dc.subjecttechnological quality
dc.titleApplication of artificial neural networks in the prediction of sugarcane juice Polen
dc.typeArtigo
dcterms.rightsHolderUniv Federal Campina Grande
unesp.author.lattes1059928253297752[3]
unesp.author.orcid0000-0002-3122-1899[3]

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