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MIA and NIR Chemical Imaging for pharmaceutical product characterization

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MIA and NIR Chemical Imaging for pharmaceutical product characterization

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dc.contributor.author Prats-Montalbán, José Manuel es_ES
dc.contributor.author Jerez-Rozo, JI es_ES
dc.contributor.author Romanach, RJ es_ES
dc.contributor.author Ferrer Riquelme, Alberto José es_ES
dc.date.accessioned 2014-10-06T12:03:47Z
dc.date.available 2014-10-06T12:03:47Z
dc.date.issued 2012-08
dc.identifier.issn 0169-7439
dc.identifier.uri http://hdl.handle.net/10251/40663
dc.description.abstract [EN] This paper presents a three step methodology based on the use of chemical oriented models (MCR and CLS) for extracting out the chemical distribution maps (CDMs) from hyperspectral images, afterwards performing multivariate image analysis (MIA) on the CDMs, and !nally extracting 'channel' and textural features from the score images related to quality characteristics These features show complementary properties to those directly obtained from the CDMs, since they take advantage of their internal correlation structure. The approach has been successfully applied to the evaluation of homogeneity and cluster presence of API in a novel formulation developed to improve the dissolution of poorly soluble drugs. © 2012 Elsevier B.V. All rights reserved. es_ES
dc.description.sponsorship Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grant DPI2011-28112-C04-02, and also by NSF-Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS, EEC-0540855) and the program NSF-Major Research Instrumentation grant 0821113.
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Chemometrics and Intelligent Laboratory Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Solubility es_ES
dc.subject Hyperspectral images es_ES
dc.subject Resolution es_ES
dc.subject MCR es_ES
dc.subject CLS es_ES
dc.subject MIA es_ES
dc.subject Texture es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title MIA and NIR Chemical Imaging for pharmaceutical product characterization es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso
dc.identifier.doi 10.1016/j.chemolab.2012.04.002
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2011-28112-C04-02/ES/MONITORIZACION, INFERENCIA, OPTIMIZACION Y CONTROL MULTI-ESCALA: DE CELULAS A BIORREACTORES. (MULTISCALES)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSF//0821113/US/MRI: Acquisition of NIR Chemical Imaging Spectrometer to Study Novel Organic Composites/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSF//0540855/US/Engineering Research Center (ERC) for Structured Organic Composites for Pharmaceutical, Nutraceutical, and Agrochemical Applications(C-SOC)/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Prats-Montalbán, JM.; Jerez-Rozo, J.; Romanach, R.; Ferrer Riquelme, AJ. (2012). MIA and NIR Chemical Imaging for pharmaceutical product characterization. Chemometrics and Intelligent Laboratory Systems. 117(117):240-249. https://doi.org/10.1016/j.chemolab.2012.04.002 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 1st African-European Conference on Chemometrics (Afrodata)
dc.relation.conferencedate September 20-24, 2010
dc.relation.conferenceplace Rabat, Morocco
dc.relation.publisherversion http://dx.doi.org/10.1016/j.chemolab.2012.04.002 es_ES
dc.description.upvformatpinicio 240 es_ES
dc.description.upvformatpfin 249 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 117 es_ES
dc.description.issue 117 es_ES
dc.relation.senia 236507
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder National Science Foundation, EEUU


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