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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/39605
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dc.creatorCarolina Sheng Whei Miawpt_BR
dc.creatorMarcelo Martins Senapt_BR
dc.creatorScheilla Vitorino Carvalho de Souzapt_BR
dc.creatorItziar Ruisanchezpt_BR
dc.creatorMaria Pilar Callaopt_BR
dc.date.accessioned2022-02-23T10:08:40Z-
dc.date.available2022-02-23T10:08:40Z-
dc.date.issued2018-
dc.citation.volume190pt_BR
dc.citation.spage55pt_BR
dc.citation.epage61pt_BR
dc.identifier.doi10.1016/j.talanta.2018.07.078pt_BR
dc.identifier.issn00399140pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/39605-
dc.description.resumoDuring the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra. PLS-DA presented better performance parameters than SIMCA in the classification of samples with just one adulterant, while poor results were achieved for samples with blends of two adulterants when using both classification methods. Three variable selection methods were tested in order to improve the effectiveness of the classification models: interval partial least squares (iPLS), variable importance in projection scores (VIP scores) and a genetic algorithm (GA). Variable selection methods improved the performance parameters for the SIMCA and PLS-DA methods when they were used to predict samples with only one adulterant. Only PLS-DA coupled with iPLS was able to classify samples with blends of two adulterants, providing sensitivity values between 100% and 83% at 100% specificity for the three studied classes.pt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentFAR - DEPARTAMENTO DE ALIMENTOSpt_BR
dc.publisher.departmentICX - DEPARTAMENTO DE QUÍMICApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofTalantapt_BR
dc.rightsAcesso Restritopt_BR
dc.subjectVariable selectionpt_BR
dc.subjectMulti-class methodspt_BR
dc.subjectPLS-DApt_BR
dc.subjectSIMCApt_BR
dc.subjectGrape nectarpt_BR
dc.subjectFood fraudpt_BR
dc.subject.otherTecnologia de alimentospt_BR
dc.subject.otherSucospt_BR
dc.titleVariable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectarspt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttp://https://www.sciencedirect.com/science/article/pii/S0039914018307859pt_BR
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