Mostrar el registro sencillo del ítem
dc.contributor.author
Garcia Reiriz, Alejandro Gabriel

dc.contributor.author
Damiani, Patricia Cecilia

dc.contributor.author
Olivieri, Alejandro Cesar

dc.date.available
2021-06-04T23:58:51Z
dc.date.issued
2010-02
dc.identifier.citation
Garcia Reiriz, Alejandro Gabriel; Damiani, Patricia Cecilia; Olivieri, Alejandro Cesar; Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 100; 2; 2-2010; 127-135
dc.identifier.issn
0169-7439
dc.identifier.uri
http://hdl.handle.net/11336/133271
dc.description.abstract
A new second-order multivariate calibration model is presented which allows one to process matrix data showing a non-linear relationship between signal and concentration, and achieving the important second-order advantage. The latter property permits analyte quantitation even in the presence of unexpected sample components, i.e., those not present in the calibration set. The model is based on a combination of residual bilinearization, which provides the second-order advantage, and kernel partial least-squares of unfolded data, a flexible non-linear version of partial least-squares. The latter one involves projection of the measured data onto a non-linear space, which in the present case consists of a set of Gaussian radial basis functions. Simulations concerning two ideal systems are analyzed: one where the signal-concentration relation is quadratic with positive deviations from linearity, and another one where it is sigmoidal. The results are favorably compared with those provided by several artificial neural network approaches. Two experimental systems are also studied, involving the analysis of: 1) the lipid degradation product malondialdehyde in olive oil samples, where the background oil provides a strong interferent signal, and 2) the antibiotic amoxicillin in the presence of the anti-inflammatory salicylate as interferent. The results for these experimental cases are also encouraging.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science

dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
KERNEL PARTIAL LEAST-SQUARES
dc.subject
RESIDUAL BILINEARIZATION
dc.subject
SECOND-ORDER ADVANTAGE
dc.subject
SECOND-ORDER CALIBRATION
dc.subject.classification
Química Analítica

dc.subject.classification
Ciencias Químicas

dc.subject.classification
CIENCIAS NATURALES Y EXACTAS

dc.title
Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-04-28T20:47:54Z
dc.journal.volume
100
dc.journal.number
2
dc.journal.pagination
127-135
dc.journal.pais
Países Bajos

dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Garcia Reiriz, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
dc.description.fil
Fil: Damiani, Patricia Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
dc.description.fil
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
dc.journal.title
Chemometrics and Intelligent Laboratory Systems

dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0169743909002093
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.chemolab.2009.11.009
Archivos asociados