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dc.contributor.author
Garcia Reiriz, Alejandro Gabriel
dc.contributor.author
Damiani, Patricia Cecilia
dc.contributor.author
Culzoni, Maria Julia
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Goicoechea, Hector Casimiro
dc.contributor.author
Olivieri, Alejandro Cesar
dc.date.available
2020-05-14T14:26:33Z
dc.date.issued
2008-05
dc.identifier.citation
Garcia Reiriz, Alejandro Gabriel; Damiani, Patricia Cecilia; Culzoni, Maria Julia; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar; A versatile strategy for achieving the second-order advantage when applying different artificial neural networks to non-linear second-order data: Unfolded principal component analysis/residual bilinearization; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 92; 1; 5-2008; 61-70
dc.identifier.issn
0169-7439
dc.identifier.uri
http://hdl.handle.net/11336/105102
dc.description.abstract
Second-order instrumental signals showing a non-linear behaviour with respect to analyte concentration can still be adequately processed in order to achieve the important second-order advantage. The combination of unfolded principal component analysis with residual bilinearization, followed by application of a variety of neural network models, allows one to obtain the second-order advantage. While principal component analysis models the training data, residual bilinearization models the contribution of the potential interferents which may be present in the test samples. Neural networks such as multilayer perceptron, radial basis functions and support vector machines, are all able to model the non-linear relationship between analyte concentrations and sample principal component scores. Three different experimental systems have been analyzed, all requiring the second-order advantage: 1) pH-UV absorbance matrices for the determination of two active principles in pharmaceutical preparations, 2) fluorescence excitation-emission matrices for the determination of polycyclic aromatic hydrocarbons, and 3) UV-induced fluorescence excitation-emission matrices for the determination of amoxicillin in the presence of salicylate. In all cases, reasonably accurate predictions can be made with the proposed techniques, which cannot be reached using traditional methods for processing second-order data.
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
Second-order advantage
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Residual bilinearization
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Multilayer perceptron networks
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Radial basis functions
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Support vector machines
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Química Analítica
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Ciencias Químicas
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CIENCIAS NATURALES Y EXACTAS
dc.title
A versatile strategy for achieving the second-order advantage when applying different artificial neural networks to non-linear second-order data: Unfolded principal component analysis/residual bilinearization
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-04-02T13:20:09Z
dc.journal.volume
92
dc.journal.number
1
dc.journal.pagination
61-70
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
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
dc.description.fil
Fil: Culzoni, Maria Julia. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimioterapia; Argentina
dc.description.fil
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimioterapia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; 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
dc.journal.title
Chemometrics and Intelligent Laboratory Systems
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.chemolab.2007.12.002
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0169743907002377
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