Simultaneous detection of multiple adulterants in ground roasted coffee by ATR-FTIR spectroscopy and data fusion

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dc.creatorNádia Reis
dc.creatorBruno Gonçalves Botelho
dc.creatorAdriana Silva Franca
dc.creatorLeandro Soares de Oliveira
dc.date.accessioned2022-02-21T20:19:03Z
dc.date.accessioned2025-09-08T23:55:26Z
dc.date.available2022-02-21T20:19:03Z
dc.date.issued2017
dc.format.mimetypepdf
dc.identifier.doi10.1007/s12161-017-0832-3
dc.identifier.issn19369751
dc.identifier.urihttps://hdl.handle.net/1843/39542
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofFood Analytical Methods
dc.rightsAcesso Restrito
dc.subjectTecnologia de alimentos
dc.subjectCafé
dc.subject.otherAdulteration
dc.subject.otherHierarchical models
dc.subject.otherCoffee
dc.subject.otherFTIR
dc.subject.otherPartial least squares discriminant analysis
dc.subject.otherData fusion
dc.titleSimultaneous detection of multiple adulterants in ground roasted coffee by ATR-FTIR spectroscopy and data fusion
dc.typeArtigo de periódico
local.citation.epage2709
local.citation.spage2700
local.citation.volume10
local.description.resumoThis paper proposes a novel screening method for the simultaneous detection of four adulterants (spent coffee grounds, roasted coffee husks, roasted corn, and roasted barley) in ground roasted coffee using partial least squares discriminant analysis (PLS-DA) with mid-infrared spectroscopy. Two different acquisition modes (attenuated total reflectance, ATR, and diffuse reflectance, DR) are compared. Two recent chemometric approaches, hierarchical models (HM) and data fusion (DF), were employed in order to improve model performance. First level models provided discrimination between unadulterated and adulterated coffee samples, whereas second level models were able to identify the presence of each specific adulterant. The use of DF decreased the percentage of misclassified samples for the first level models from 19.6/14.7% (DR) and 7.5/14.5% (ATR) down to 2.5/4.5% considering the training/test sets. The percentage of misclassified samples in the second level models went as low as 0% (DF—spent coffee, training set). The proposed method is simple, fast, reliable for detecting adulteration in coffee samples, and capable of identifying these adulterants, even when in complex mixtures containing other adulterants.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA MECÂNICA
local.publisher.departmentFAR - DEPARTAMENTO DE ALIMENTOS
local.publisher.departmentICX - DEPARTAMENTO DE QUÍMICA
local.publisher.initialsUFMG
local.url.externahttps://link.springer.com/content/pdf/10.1007/s12161-017-0832-3.pdf

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