Comparison of different multivariate classification methods for the detection of adulterations in grape nectars by using low-field nuclear magnetic resonance

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dc.creatorCarolina Sheng Whei Miaw
dc.creatorPoliana Macedo Santos
dc.creatorAlessandro Rangel Carolino Sales Silva
dc.creatorAline Gozzi
dc.creatorNilson César Castanheira Guimarães
dc.creatorMaria Pilar Callao
dc.creatorItziar Ruisánchez
dc.creatorMarcelo Martins de Sena
dc.creatorScheilla Vitorino Carvalho de Souza
dc.date.accessioned2022-05-19T18:34:03Z
dc.date.accessioned2025-09-08T22:55:29Z
dc.date.available2022-05-19T18:34:03Z
dc.date.issued2020
dc.identifier.doi10.1007/s12161-019-01522-7
dc.identifier.issn1936-9751
dc.identifier.urihttps://hdl.handle.net/1843/41830
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofFood Analytical Methods
dc.rightsAcesso Restrito
dc.subjectTecnologia de alimentos
dc.subjectUva
dc.subjectAlteração em néctar de uva
dc.subjectRessonância magnética nuclear de baixo campo
dc.subjectNéctar de fruta
dc.subject.otherTime-domain NMR
dc.subject.otherTD-NMR
dc.subject.otherOne-class modeling
dc.subject.otherDiscriminant analysis
dc.subject.otherFruit nectar
dc.subject.otherFood adulteration
dc.titleComparison of different multivariate classification methods for the detection of adulterations in grape nectars by using low-field nuclear magnetic resonance
dc.typeArtigo de periódico
local.citation.epage118
local.citation.spage108
local.citation.volume13
local.description.resumoGrape is the most consumed nectar in Brazil and a relatively expensive beverage. Therefore, it is susceptible to fraud by substitution with other less expensive fruit juices. Adulterations of grape nectars by the addition of apple juice, cashew juice, and mixtures of both were evaluated by using low-field nuclear magnetic resonance (LF-NMR) and supervised multivariate classification methods. Two different approaches were investigated using one-class (only unadulterated samples (UN) were modeled) and multiclass (three classes were modeled: UN, adulterated with cashew (CAS), and adulterated with apple (APP)) strategies. For the one-class approach, soft independent modeling of class analogy (SIMCA), one-class partial least squares (OCPLS), and data-driven SIMCA (DD-SIMCA) models were built. For the multiclass approach, partial least squares discriminant analysis (PLS-DA) and multiclass SIMCA models were built. The results obtained demonstrated good performances by all the one-class methods with efficiency rates higher than 93%. For the multiclass approach, the classification of samples containing only one type of adulterant presented efficiencies higher than 90% and 97% using SIMCA and PLS-DA, respectively. The classification of samples containing blends of two adulterants was satisfactory for the CAS class, but not for the APP class when applying PLS-DA. Nevertheless, multiclass SIMCA did not provide satisfactory predictions for either of these two classes.
local.publisher.countryBrasil
local.publisher.departmentFAR - DEPARTAMENTO DE ALIMENTOS
local.publisher.departmentICX - DEPARTAMENTO DE QUÍMICA
local.publisher.initialsUFMG
local.url.externahttps://link.springer.com/article/10.1007/s12161-019-01522-7

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