Wine classification by taste sensors made from ultra-thin films and using neural networks

Página do item simplificado
dc.contributor.authorRiul, A.
dc.contributor.authorde Sousa, H. C.
dc.contributor.authorMalmegrim, R. R.
dc.contributor.authordos Santos, D. S.
dc.contributor.authorCarvalho, ACPLF
dc.contributor.authorFonseca, F. J.
dc.contributor.authorOliveira, O. N.
dc.contributor.authorMattoso, LHC
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.date.accessioned2014-05-20T13:22:54Z
dc.date.available2014-05-20T13:22:54Z
dc.date.issued2004-03-01
dc.description.abstractThis paper reports on a sensor array able to distinguish tastes and used to classify red wines. The array comprises sensing units made from Langmuir-Blodgett (LB) films of conducting polymers and lipids and layer-by-layer (LBL) films from chitosan deposited onto gold interdigitated electrodes. Using impedance spectroscopy as the principle of detection, we show that distinct clusters can be identified in principal component analysis (PCA) plots for six types of red wine. Distinction can be made with regard to vintage, vineyard and brands of the red wine. Furthermore, if the data are treated with artificial neural networks (ANNs), this artificial tongue can identify wine samples stored under different conditions. This is illustrated by considering 900 wine samples, obtained with 30 measurements for each of the five bottles of the six wines, which could be recognised with 100% accuracy using the algorithms Standard Backpropagation and Backpropagation momentum in the ANNs. (C) 2003 Elsevier B.V. All rights reserved.en
dc.description.affiliationUNESP, FCT, Dept Fis Quim & Biol, BR-19060900 Presidente Prudente, SP, Brazil
dc.description.affiliationUSP, Inst Ciências Matemat & Comp, BR-13560970 Sao Carlos, SP, Brazil
dc.description.affiliationEMBRAPA, Instrumentacao Agropecuaria, BR-13560970 Sao Carlos, SP, Brazil
dc.description.affiliationUSP, Inst Fis Sao Carlos, BR-13560970 Sao Carlos, SP, Brazil
dc.description.affiliationUniv São Paulo, Escola Politecn, BR-05508900 São Paulo, Brazil
dc.description.affiliationUnespUNESP, FCT, Dept Fis Quim & Biol, BR-19060900 Presidente Prudente, SP, Brazil
dc.format.extent77-82
dc.identifierhttp://dx.doi.org/10.1016/j.snb.2003.09.025
dc.identifier.citationSensors and Actuators B-chemical. Lausanne: Elsevier B.V. Sa, v. 98, n. 1, p. 77-82, 2004.
dc.identifier.doi10.1016/j.snb.2003.09.025
dc.identifier.issn0925-4005
dc.identifier.lattes5919252907988514
dc.identifier.urihttp://hdl.handle.net/11449/6800
dc.identifier.wosWOS:000220132700013
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofSensors and Actuators B: Chemical
dc.relation.ispartofjcr5.667
dc.relation.ispartofsjr1,406
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectconducting polymerspt
dc.subjecttastept
dc.subjectwinespt
dc.subjectartificial neural networkspt
dc.subjectelectronic tonguept
dc.titleWine classification by taste sensors made from ultra-thin films and using neural networksen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes5919252907988514
unesp.author.orcid0000-0002-4765-6459[5]
unesp.author.orcid0000-0002-9760-1851[1]
unesp.author.orcid0000-0001-7586-1014[8]
unesp.author.orcid0000-0002-5399-5860[7]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências e Tecnologia, Presidente Prudentept

Arquivos

Licença do Pacote
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:

Coleções

Artigos - Física, Química e Biologia - FCT