Non-supervised pattern recognition methods: Exploring chemometrical procedures for evaluating analytical data

dc.creatorCorreia, PRM
dc.creatorFerreira, MMC
dc.date2007
dc.dateMAR-APR
dc.date2014-11-14T20:47:05Z
dc.date2015-11-26T16:08:19Z
dc.date2014-11-14T20:47:05Z
dc.date2015-11-26T16:08:19Z
dc.date.accessioned2018-03-28T22:56:53Z
dc.date.available2018-03-28T22:56:53Z
dc.identifierQuimica Nova. Soc Brasileira Quimica, v. 30, n. 2, n. 481, n. 487, 2007.
dc.identifier0100-4042
dc.identifierWOS:000244752500042
dc.identifier10.1590/S0100-40422007000200042
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/62091
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/62091
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/62091
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1266330
dc.descriptionAn activity for introducing hierarchical cluster analysis (HCA) and principal component analysis (PCA) during the Instrumental Analytical Chemistry course is presented. The posed problem involves the discrimination of mineral water samples according to their geographical origin. Thirty-seven samples of 9 different brands were considered and the results from the determination of Na, K, Mg, Ca, Sr and Ba were taken into account. Non-supervised methods for pattern recognition were explored to construct a dendrogram, score and loading plots. The devised activity can be adopted for introducing Chemometrics devoted to data handling, stressing its importance in the context of modem Analytical Chemistry.
dc.description30
dc.description2
dc.description481
dc.description487
dc.languagept
dc.publisherSoc Brasileira Quimica
dc.publisherSao Paulo
dc.publisherBrasil
dc.relationQuimica Nova
dc.relationQuim. Nova
dc.rightsaberto
dc.sourceWeb of Science
dc.subjectanalytical chemistry
dc.subjectchemometrics
dc.subjectpattern recognition
dc.subjectCv Pinot-noir
dc.subjectExploratory Analysis
dc.subjectUnited-states
dc.subjectTutorial
dc.subjectFood
dc.subjectAuthentication
dc.subjectClassification
dc.subjectWaters
dc.subjectFrance
dc.subjectWines
dc.titleNon-supervised pattern recognition methods: Exploring chemometrical procedures for evaluating analytical data
dc.typeArtículos de revistas


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