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dc.contributor.author
Garcia Reiriz, Alejandro Gabriel  
dc.contributor.author
Damiani, Patricia Cecilia  
dc.contributor.author
Culzoni, Maria Julia  
dc.contributor.author
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  
dc.subject
Residual bilinearization  
dc.subject
Multilayer perceptron networks  
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Radial basis functions  
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Support vector machines  
dc.subject.classification
Química Analítica  
dc.subject.classification
Ciencias Químicas  
dc.subject.classification
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