Detection of several common adulterants in raw milk by mid-infrared spectroscopy and one-class and multi-class multivariate strategies

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dc.creatorCarina de Souza Gondim
dc.creatorRoberto Gonçalves Junqueira
dc.creatorScheilla Vitorino Carvalho de Souza
dc.creatorItziar Ruisánchez
dc.creatorMaria Pilar Callao
dc.date.accessioned2022-03-28T20:14:18Z
dc.date.accessioned2025-09-09T00:08:10Z
dc.date.available2022-03-28T20:14:18Z
dc.date.issued2017-09
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.format.mimetypepdf
dc.identifier.doi10.1016/j.foodchem.2017.03.022
dc.identifier.issn03088146
dc.identifier.urihttps://hdl.handle.net/1843/40513
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofFood Chemistry
dc.rightsAcesso Aberto
dc.subjectTecnologia de alimentos
dc.subjectLeite
dc.subject.otherMilk adulteration
dc.subject.otherOne-class modelling
dc.subject.otherAdulterant detection
dc.subject.otherMulti-class modelling
dc.subject.otherMultivariate SIMCA screening
dc.titleDetection of several common adulterants in raw milk by mid-infrared spectroscopy and one-class and multi-class multivariate strategies
dc.typeArtigo de periódico
local.citation.epage75
local.citation.spage68
local.citation.volume230
local.description.resumoA sequential strategy was proposed to detect adulterants in milk using a mid-infrared spectroscopy and soft independent modelling of class analogy technique. Models were set with low target levels of adulterations including formaldehyde (0.074 g.L−1), hydrogen peroxide (21.0 g.L−1), bicarbonate (4.0 g.L−1), carbonate (4.0 g.L−1), chloride (5.0 g.L−1), citrate (6.5 g.L−1), hydroxide (4.0 g.L−1), hypochlorite (0.2 g.L−1), starch (5.0 g.L−1), sucrose (5.4 g.L−1) and water (150 g.L−1). In the first step, a one-class model was developed with unadulterated samples, providing 93.1% sensitivity. Four poorly assigned adulterants were discarded for the following step (multi-class modelling). Then, in the second step, a multi-class model, which considered unadulterated and formaldehyde-, hydrogen peroxide-, citrate-, hydroxide- and starch-adulterated samples was implemented, providing 82% correct classifications, 17% inconclusive classifications and 1% misclassifications. The proposed strategy was considered efficient as a screening approach since it would reduce the number of samples subjected to confirmatory analysis, time, costs and errors.
local.publisher.countryBrasil
local.publisher.departmentFAR - DEPARTAMENTO DE ALIMENTOS
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0308814617303874

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