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Zastosuj identyfikator do podlinkowania lub zacytowania tej pozycji: http://hdl.handle.net/20.500.12128/21741
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DC poleWartośćJęzyk
dc.contributor.authorPiszczek, Łukasz-
dc.contributor.authorDaszykowski, Michał-
dc.date.accessioned2021-10-07T10:59:50Z-
dc.date.available2021-10-07T10:59:50Z-
dc.date.issued2021-
dc.identifier.citation"Measurement" (2021), Vol. 186, art. no. 110201, s. 1-10pl_PL
dc.identifier.issn0263-2241-
dc.identifier.urihttp://hdl.handle.net/20.500.12128/21741-
dc.description.abstractThis study examines the potential of near-infrared hyperspectral imaging for assessing the size of polymer particles in model fractions based on the scattering phenomena. Different fractions of ground polymers, either polymethyl methacrylate or polypropylene, were characterized by near-infrared spectra collected between 900 and 1700 nm. The possibility to estimate the size of polymer particles using hyperspectral images was confronted with a basic single spot near-infrared measurement. Hyperspectral imaging, in addition to the standard spectral data dimension, provides information about the spatial distribution of sample components and reveals changes in physical properties. Therefore, one can gain a better insight into the scattering phenomena and study the physical inhomogeneity of a sample in terms of particle size distribution. The partial least-squares models constructed to estimate particle size of polymers that were characterized by hyperspectral images (a pixel-based approach) outperforms models built for mean spectra regardless of the considered powdered polymer.pl_PL
dc.language.isoenpl_PL
dc.rightsUznanie autorstwa 3.0 Polska*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/pl/*
dc.subjectParticle size distributionpl_PL
dc.subjectHyperspectral imagingpl_PL
dc.subjectPolymer detectionpl_PL
dc.subjectPolymer processingpl_PL
dc.subjectChemometricspl_PL
dc.titleNear-infrared hyperspectral imaging for polymer particle size estimationpl_PL
dc.typeinfo:eu-repo/semantics/articlepl_PL
dc.identifier.doi10.1016/j.measurement.2021.110201-
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Uznanie Autorstwa 3.0 Polska Creative Commons Creative Commons