Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars

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dc.creatorCarolina Sheng Whei Miaw
dc.creatorMarcelo Martins Sena
dc.creatorScheilla Vitorino Carvalho de Souza
dc.creatorItziar Ruisanchez
dc.creatorMaria Pilar Callao
dc.date.accessioned2022-02-23T10:08:40Z
dc.date.accessioned2025-09-09T00:37:02Z
dc.date.available2022-02-23T10:08:40Z
dc.date.issued2018
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.format.mimetypepdf
dc.identifier.doi10.1016/j.talanta.2018.07.078
dc.identifier.issn00399140
dc.identifier.urihttps://hdl.handle.net/1843/39605
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofTalanta
dc.rightsAcesso Restrito
dc.subjectTecnologia de alimentos
dc.subjectSucos
dc.subject.otherVariable selection
dc.subject.otherMulti-class methods
dc.subject.otherPLS-DA
dc.subject.otherSIMCA
dc.subject.otherGrape nectar
dc.subject.otherFood fraud
dc.titleVariable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars
dc.typeArtigo de periódico
local.citation.epage61
local.citation.spage55
local.citation.volume190
local.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.
local.publisher.countryBrasil
local.publisher.departmentFAR - DEPARTAMENTO DE ALIMENTOS
local.publisher.departmentICX - DEPARTAMENTO DE QUÍMICA
local.publisher.initialsUFMG
local.url.externahttp://https://www.sciencedirect.com/science/article/pii/S0039914018307859

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