Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends.

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dc.contributorDAVY WILLIAM HIDALGO CHÁVEZ, UFRRJ; FELIPE LEITE COELHO DA SILVA, UFRRJ; RENAN VICENTE PINTO, UFRRJ; CARLOS WANDERLEI PILER DE CARVALHO, CTAA; OTNIEL FREITAS SILVA, CTAA.
dc.creatorHIDALGO CHÁVEZ, D. W.
dc.creatorSILVA, F. L. C. DA
dc.creatorPINTO, R. V.
dc.creatorCARVALHO, C. W. P. de
dc.creatorFREITAS-SILVA, O.
dc.date2023-10-16T19:25:03Z
dc.date2023-10-16T19:25:03Z
dc.date2023-10-16
dc.date2023
dc.date.accessioned2026-06-30T23:10:32Z
dc.descriptionThis article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the e feasibility of using these two simple multivariate statistical techniques for image classification.
dc.identifierCyTA: Journal of Food, v. 21, n. 1, p. 606-613, 2023.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1157235
dc.identifierhttps://doi.org/10.1080/19476337.2023.2263513
dc.identifier.urihttp://hdl.handle.net/123456789/375801
dc.languageeng
dc.rightsopenAccess
dc.subjectImage classification
dc.subjectImage analysis
dc.subjectPrincipal component analysis
dc.titleStreamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends.
dc.typeArtigo de periódico

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