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Two sides of the same coin: kernel partial least-squares (KPLS) for linear and non-linear multivariate calibration. A tutorial

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dc.citation.titleTalanta Open
dc.citation.volume7
dc.creatorAllegrini, Franco
dc.creatorOlivieri, Alejandro César
dc.date.accessioned2023-08-14T13:04:00Z
dc.date.available2023-08-14T13:04:00Z
dc.date.issued2023-08
dc.descriptionA tutorial is presented on the operation and properties of the non-linear multivariate regression model kernel partial least-squares (KPLS). After the discussion of a simple non-linear univariate problem, solved by regressing a dependent variable on the projection of an independent variable onto a set of Gaussian functions, the principles of KPLS are introduced for processing non-linear multivariate data. The following aspects are covered: (1) the estimation of the model sensitivity as a function of analyte concentration from error propagation concepts, (2) the proposal of a parameter measuring the degree of non-linearity, to avoid a black-and-white description of data sets as either linear or non-linear, (3) the use of the model parameters for variable selection. The application of KPLS to both simulated and experimental data sets is discussed, in the latter case involving near infrared spectra employed for the determination of quality parameters in foodstuff samples and fluorescence spectroscopic data for the study of systems of biological relevance. Computer codes written in the popular MATLAB and R environments are also provided.es
dc.description.filFil: Allegrini, Franco. Universidad Nacional de Rosario, Instituto de Química Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina.
dc.description.filFil: Olivieri, Alejandro César. Universidad Nacional de Rosario, Instituto de Química Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina.
dc.description.sponsorshipUniversidad Nacional de Rosario (UNR): Project 80020190100037UR
dc.description.sponsorshipConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
dc.description.sponsorshipAgencia Nacional de Promoción Científica y Tecnológica (ANPCyT): Project PICT 2020-00179
dc.formatapplication/pdf
dc.format.extent1-15
dc.identifier.issn2666-8319
dc.identifier.urihttp://hdl.handle.net/2133/26186
dc.language.isoenges
dc.publisherElsevier B.V.es
dc.relation.publisherversionhttps://doi.org/10.1016/j.talo.2023.100235
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2666831923000553?via%3Dihub
dc.rightsopenAccesses
dc.rights.holderAllegrini, Francoes
dc.rights.holderOlivieri, Alejandro Césares
dc.rights.holderUniversidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas
dc.rights.textAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)es
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectKernel partial least-squareses
dc.subjectMultivariate calibrationes
dc.subjectNear infrared spectraes
dc.subjectFluorescence dataes
dc.subjectDetection of non-linearityes
dc.titleTwo sides of the same coin: kernel partial least-squares (KPLS) for linear and non-linear multivariate calibration. A tutoriales
dc.typearticle
dc.typeartículo
dc.typepublishedVersion
dc.type.collectionarticulo
dc.type.versionpublishedVersion

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