Determination of allura red dye in hard candies by using digital images obtained with a mobile phone and N-PLS

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dc.creatorBruno Gonçalves Botelho
dc.creatorKele Cristina Ferreira Dantas
dc.creatorMarcelo Martins de Sena
dc.date.accessioned2022-02-21T19:48:17Z
dc.date.accessioned2025-09-09T00:02:46Z
dc.date.available2022-02-21T19:48:17Z
dc.date.issued2017
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.format.mimetypepdf
dc.identifier.doi10.1016/j.chemolab.2017.05.004
dc.identifier.issn01697439
dc.identifier.urihttps://hdl.handle.net/1843/39530
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofChemometrics and Intelligent Laboratory Systems
dc.rightsAcesso Restrito
dc.subjectTecnologia de alimentos
dc.subject.otherMultivariate image analysis
dc.subject.otherFood dyes
dc.subject.otherCell phone
dc.subject.otherOptical sensor
dc.subject.otherChemometrics
dc.subject.otherFood quality control
dc.titleDetermination of allura red dye in hard candies by using digital images obtained with a mobile phone and N-PLS
dc.typeArtigo de periódico
local.citation.epage49
local.citation.spage44
local.citation.volume167
local.description.resumoThis paper describes the development of an optical sensor device using a smartphone and a homemade dark chamber built with recycled materials. This low cost instrument was employed in the development of multivariate image regression methods for the determination of the azo dye allura red in hard candies. To build the models, 238 candy samples of four flavors and different brands and batches were used. Firstly, a multivariate calibration model using RGB histograms and partial least squares (PLS) was built. This model provided high prediction errors, which were attributed to the presence of textural variations in the images. Then, a more complex image analysis methodology that incorporates spatial information, and consists of preprocessing by a two-dimensional fast Fourier transform followed by multi-way calibration with N-way PLS, provided better results, decreasing the prediction errors around 25–35%. The final model was submitted to a complete multivariate analytical validation, being considered precise, linear, sensitive and unbiased. The analytical range was established between 22.9 and 78.8 mg kg−1 of allura red. Root mean square errors of calibration (RMSEC) and prediction (RMSEP) of 4.8 and 6.1 mg kg−1 were estimated. The developed method is simple, rapid, and nondestructive.
local.publisher.countryBrasil
local.publisher.departmentFAR - DEPARTAMENTO DE ALIMENTOS
local.publisher.departmentICX - DEPARTAMENTO DE QUÍMICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0169743916304956

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