Estimation of percentage of impurities in coffee using a computer vision system

dc.contributor.authorCosta, Anderson G.
dc.contributor.authorSilva, Eudócio R. O. da
dc.contributor.authorBarros, Murilo M. de
dc.contributor.authorFagundes, Jonatthan A.
dc.date.accessioned2022-12-02T13:39:13Z
dc.date.available2022-12-02T13:39:13Z
dc.date.issued2022-01-14
dc.description.abstractThe quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.pt_BR
dc.formatpdfpt_BR
dc.identifier.citationCOSTA, Anderson G.; SILVA, Eudócio R. O. da; BARROS, Murilo M. de; FAGUNDES, Jonatthan A. Estimation of percentage of impurities in coffee using a computer vision system. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 26, n. 2, p. 142-148, 14 jan. 2022. Available from: https://doi.org/10.1590/1807-1929/agriambi.v26n2p142-148. Accessed: 2 dec. 2022.pt_BR
dc.identifier.issn1807-1929
dc.identifier.uriDOI: http://dx.doi.org/10.1590/1807-1929/agriambi.v26n2p142-148pt_BR
dc.identifier.urihttp://www.sbicafe.ufv.br/handle/123456789/13684
dc.language.isoenpt_BR
dc.publisherDepartamento de Engenharia Agrícola - UFCGpt_BR
dc.relation.ispartofseriesRevista Brasileira de Engenharia Agrícola e Ambiental;v. 26, n. 2, p. 142-148, 2022;
dc.rightsOpen Accesspt_BR
dc.subjectcoffee qualitypt_BR
dc.subjectpostharvestpt_BR
dc.subjectprincipal component regressionpt_BR
dc.subjectimage descriptorspt_BR
dc.subjectnon-destructive methodpt_BR
dc.subject.classificationCafeicultura::Qualidade de bebidapt_BR
dc.titleEstimation of percentage of impurities in coffee using a computer vision systempt_BR
dc.typeArtigopt_BR

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