Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration

Autores
Braga, Jez Willian Batista; Allegrini, Franco; Olivieri, Alejandro Cesar
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A maximum likelihood model is described for performing second-order multivariate calibration with unfolded principal component regression with residual bilinearization (MLU-PCR/RBL). It differs from the conventional RBL models based on U-PCR or U-PLS (unfolded partial least-squares) in the incorporation of the measurement error information into both the U-PCR calibration and the RBL model phases. The error information is represented by the instrumental error covariance matrix. Simulations were made by adding correlated and proportional noise to synthetic systems consisting of one analyte in the presence of a calibrated and unexpected interferent, under different conditions of overlapping profiles, noise levels and noise types (correlated and proportional). The results show that MLU-PCR/RBL outperforms conventional RBL methods in prediction ability, as confirmed by a detailed study on validation samples through the average prediction error as a convenient figure of merit. Results obtained in experimental data set based on flow injection analysis and UV detection for determination of acetylsalicylic and ascorbic acids in pharmaceutical products also support the theoretical conclusions.
Fil: Braga, Jez Willian Batista. Universidade do Brasília; Brasil. 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
Fil: Allegrini, Franco. 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
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
Materia
Error Covariance Matrix
Maximum Likelihood Principal Component Regression
Residual Bilinearization
Second-Order Multivariate Calibration
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/50433
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/50433
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibrationBraga, Jez Willian BatistaAllegrini, FrancoOlivieri, Alejandro CesarError Covariance MatrixMaximum Likelihood Principal Component RegressionResidual BilinearizationSecond-Order Multivariate Calibrationhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A maximum likelihood model is described for performing second-order multivariate calibration with unfolded principal component regression with residual bilinearization (MLU-PCR/RBL). It differs from the conventional RBL models based on U-PCR or U-PLS (unfolded partial least-squares) in the incorporation of the measurement error information into both the U-PCR calibration and the RBL model phases. The error information is represented by the instrumental error covariance matrix. Simulations were made by adding correlated and proportional noise to synthetic systems consisting of one analyte in the presence of a calibrated and unexpected interferent, under different conditions of overlapping profiles, noise levels and noise types (correlated and proportional). The results show that MLU-PCR/RBL outperforms conventional RBL methods in prediction ability, as confirmed by a detailed study on validation samples through the average prediction error as a convenient figure of merit. Results obtained in experimental data set based on flow injection analysis and UV detection for determination of acetylsalicylic and ascorbic acids in pharmaceutical products also support the theoretical conclusions.Fil: Braga, Jez Willian Batista. Universidade do Brasília; Brasil. 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; ArgentinaFil: Allegrini, Franco. 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; ArgentinaFil: 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; ArgentinaElsevier Science2017-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/50433Braga, Jez Willian Batista ; Allegrini, Franco; Olivieri, Alejandro Cesar; Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 170; 11-2017; 51-570169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2017.09.016info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743917302150info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:45:07Zoai:ri.conicet.gov.ar:11336/50433instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:45:07.749CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration
title Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration
spellingShingle Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration
Braga, Jez Willian Batista
Error Covariance Matrix
Maximum Likelihood Principal Component Regression
Residual Bilinearization
Second-Order Multivariate Calibration
title_short Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration
title_full Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration
title_fullStr Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration
title_full_unstemmed Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration
title_sort Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration
dc.creator.none.fl_str_mv Braga, Jez Willian Batista
Allegrini, Franco
Olivieri, Alejandro Cesar
author Braga, Jez Willian Batista
author_facet Braga, Jez Willian Batista
Allegrini, Franco
Olivieri, Alejandro Cesar
author_role author
author2 Allegrini, Franco
Olivieri, Alejandro Cesar
author2_role author
author
dc.subject.none.fl_str_mv Error Covariance Matrix
Maximum Likelihood Principal Component Regression
Residual Bilinearization
Second-Order Multivariate Calibration
topic Error Covariance Matrix
Maximum Likelihood Principal Component Regression
Residual Bilinearization
Second-Order Multivariate Calibration
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A maximum likelihood model is described for performing second-order multivariate calibration with unfolded principal component regression with residual bilinearization (MLU-PCR/RBL). It differs from the conventional RBL models based on U-PCR or U-PLS (unfolded partial least-squares) in the incorporation of the measurement error information into both the U-PCR calibration and the RBL model phases. The error information is represented by the instrumental error covariance matrix. Simulations were made by adding correlated and proportional noise to synthetic systems consisting of one analyte in the presence of a calibrated and unexpected interferent, under different conditions of overlapping profiles, noise levels and noise types (correlated and proportional). The results show that MLU-PCR/RBL outperforms conventional RBL methods in prediction ability, as confirmed by a detailed study on validation samples through the average prediction error as a convenient figure of merit. Results obtained in experimental data set based on flow injection analysis and UV detection for determination of acetylsalicylic and ascorbic acids in pharmaceutical products also support the theoretical conclusions.
Fil: Braga, Jez Willian Batista. Universidade do Brasília; Brasil. 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
Fil: Allegrini, Franco. 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
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
description A maximum likelihood model is described for performing second-order multivariate calibration with unfolded principal component regression with residual bilinearization (MLU-PCR/RBL). It differs from the conventional RBL models based on U-PCR or U-PLS (unfolded partial least-squares) in the incorporation of the measurement error information into both the U-PCR calibration and the RBL model phases. The error information is represented by the instrumental error covariance matrix. Simulations were made by adding correlated and proportional noise to synthetic systems consisting of one analyte in the presence of a calibrated and unexpected interferent, under different conditions of overlapping profiles, noise levels and noise types (correlated and proportional). The results show that MLU-PCR/RBL outperforms conventional RBL methods in prediction ability, as confirmed by a detailed study on validation samples through the average prediction error as a convenient figure of merit. Results obtained in experimental data set based on flow injection analysis and UV detection for determination of acetylsalicylic and ascorbic acids in pharmaceutical products also support the theoretical conclusions.
publishDate 2017
dc.date.none.fl_str_mv 2017-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/50433
Braga, Jez Willian Batista ; Allegrini, Franco; Olivieri, Alejandro Cesar; Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 170; 11-2017; 51-57
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/50433
identifier_str_mv Braga, Jez Willian Batista ; Allegrini, Franco; Olivieri, Alejandro Cesar; Maximum likelihood unfolded principal component regression with residual bilinearization (MLU-PCR/RBL) for second-order multivariate calibration; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 170; 11-2017; 51-57
0169-7439
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2017.09.016
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743917302150
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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