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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/39597
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dc.creatorAna Carolina da Costa Fulgênciopt_BR
dc.creatorVinícius Pires Teixeira de Araújopt_BR
dc.creatorHebert Vinicius Pereirapt_BR
dc.creatorBruno Gonçalves Botelhopt_BR
dc.creatorMarcelo Martins de Senapt_BR
dc.date.accessioned2022-02-23T04:05:48Z-
dc.date.available2022-02-23T04:05:48Z-
dc.date.issued2019-
dc.citation.volume13pt_BR
dc.citation.issue1pt_BR
dc.citation.spage303pt_BR
dc.citation.epage312pt_BR
dc.identifier.doi10.1007/s12161-019-01634-0pt_BR
dc.identifier.issn19369751pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/39597-
dc.description.resumoColor is an important sensory parameter required for the quality control of beers. A new multivariate image analysis method for the color determination of beers was proposed and validated. Reference color values were determined using the SRM (standard reference method) system, which is based on absorbance measurements at 430 nm. Digital images were obtained with an iPhone 7 smartphone. The obtained RGB histograms were used for building partial least squares (PLS) models. The developed method is direct, simple, and rapid, not requiring sample pretreatment steps as the reference method. Beer samples of different styles, brands, and brewery companies were obtained in a large variety, totalizing 128 samples and comprising a range from 3 to 130 SRM units. A global PLS model built with all the beer samples presented too large prediction errors for some samples in the lower part of the SRM scale (below 12 units). Thus, considering the requirement of dilution prescribed by the reference method for samples with absorbances higher than 1.0, two local calibration models were built: for high SRM range (above 12 units) and low SRM range (equal or below 12 units) samples. A previous PLS discriminant analysis (PLS-DA) model was used to assign the beer samples to these two classes, resulting in 78 and 50 samples in the high- and low-range models, respectively. These two models were validated according to the Brazilian and international guidelines, being considered linear, accurate, precise, and unbiased. Uncertainties were also calculated for estimating confidence intervals for the predictions of the validation samples. The developed method could be easily adapted in a mobile platform, spreading its use and opening the possibility for the commercial production of a dedicated equipment.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_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.ispartofFood Analytical Methodspt_BR
dc.rightsAcesso Restritopt_BR
dc.subjectRGB histogramspt_BR
dc.subjectImage analysispt_BR
dc.subjectPLSpt_BR
dc.subjectMultivariate calibrationpt_BR
dc.subjectBeer quality controlpt_BR
dc.subjectChemometricspt_BR
dc.subject.otherTecnologia de alimentospt_BR
dc.subject.otherCervejapt_BR
dc.titleDevelopment of a simple and rapid method for color determination in beers using digital imagespt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://link.springer.com/article/10.1007/s12161-019-01634-0pt_BR
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