Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization

Autores
Allegrini, Franco; Olivieri, Alejandro Cesar
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A new expression is developed which allows estimating the sensitivity for the whole family of multivariate calibration algorithms based on partial least-squares regression combined with residual multilinearization. The sensitivity can be employed to compute other relevant figures of merit such as analytical sensitivity, limit of detection, limit of quantitation, and uncertainty in predicted concentration. The results are substantiated by extensive Monte Carlo noise addition simulations for a variety of systems with a different number of analytes and interfering agents, different degrees of overlapping in component profiles, and different numbers of instrumental data modes per sample, all requiring the achievement of the second-order advantage. The connection between the present approach and the intuitive concept of net analyte signal is discussed. An experimental example for which second-, third-, and fourth-order data are available is also studied, concerning the improvement in figures of merit on increasing the data order, which is consistent with the decrease in average prediction error.
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
Figures of Merit
Sensitivity
Partial Least Squares
Multi-way calibration
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/106476
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Analytical Figures of Merit for Partial Least-Squares Coupled to Residual MultilinearizationAllegrini, FrancoOlivieri, Alejandro CesarFigures of MeritSensitivityPartial Least SquaresMulti-way calibrationhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A new expression is developed which allows estimating the sensitivity for the whole family of multivariate calibration algorithms based on partial least-squares regression combined with residual multilinearization. The sensitivity can be employed to compute other relevant figures of merit such as analytical sensitivity, limit of detection, limit of quantitation, and uncertainty in predicted concentration. The results are substantiated by extensive Monte Carlo noise addition simulations for a variety of systems with a different number of analytes and interfering agents, different degrees of overlapping in component profiles, and different numbers of instrumental data modes per sample, all requiring the achievement of the second-order advantage. The connection between the present approach and the intuitive concept of net analyte signal is discussed. An experimental example for which second-, third-, and fourth-order data are available is also studied, concerning the improvement in figures of merit on increasing the data order, which is consistent with the decrease in average prediction error.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; 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; ArgentinaAmerican Chemical Society2012-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/106476Allegrini, Franco; Olivieri, Alejandro Cesar; Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization; American Chemical Society; Analytical Chemistry; 84; 24; 12-2012; 10823-108300003-2700CONICET DigitalCONICETenginfo:eu-repo/semantics/reference/url/https://pubs.acs.org/doi/suppl/10.1021/ac302996d/suppl_file/ac302996d_si_001.pdfinfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/ac302996dinfo:eu-repo/semantics/altIdentifier/doi/10.1021/ac302996dinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:27:34Zoai:ri.conicet.gov.ar:11336/106476instacron: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:34982025-10-15 14:27:34.32CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization
title Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization
spellingShingle Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization
Allegrini, Franco
Figures of Merit
Sensitivity
Partial Least Squares
Multi-way calibration
title_short Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization
title_full Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization
title_fullStr Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization
title_full_unstemmed Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization
title_sort Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization
dc.creator.none.fl_str_mv Allegrini, Franco
Olivieri, Alejandro Cesar
author Allegrini, Franco
author_facet Allegrini, Franco
Olivieri, Alejandro Cesar
author_role author
author2 Olivieri, Alejandro Cesar
author2_role author
dc.subject.none.fl_str_mv Figures of Merit
Sensitivity
Partial Least Squares
Multi-way calibration
topic Figures of Merit
Sensitivity
Partial Least Squares
Multi-way 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 new expression is developed which allows estimating the sensitivity for the whole family of multivariate calibration algorithms based on partial least-squares regression combined with residual multilinearization. The sensitivity can be employed to compute other relevant figures of merit such as analytical sensitivity, limit of detection, limit of quantitation, and uncertainty in predicted concentration. The results are substantiated by extensive Monte Carlo noise addition simulations for a variety of systems with a different number of analytes and interfering agents, different degrees of overlapping in component profiles, and different numbers of instrumental data modes per sample, all requiring the achievement of the second-order advantage. The connection between the present approach and the intuitive concept of net analyte signal is discussed. An experimental example for which second-, third-, and fourth-order data are available is also studied, concerning the improvement in figures of merit on increasing the data order, which is consistent with the decrease in average prediction error.
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 new expression is developed which allows estimating the sensitivity for the whole family of multivariate calibration algorithms based on partial least-squares regression combined with residual multilinearization. The sensitivity can be employed to compute other relevant figures of merit such as analytical sensitivity, limit of detection, limit of quantitation, and uncertainty in predicted concentration. The results are substantiated by extensive Monte Carlo noise addition simulations for a variety of systems with a different number of analytes and interfering agents, different degrees of overlapping in component profiles, and different numbers of instrumental data modes per sample, all requiring the achievement of the second-order advantage. The connection between the present approach and the intuitive concept of net analyte signal is discussed. An experimental example for which second-, third-, and fourth-order data are available is also studied, concerning the improvement in figures of merit on increasing the data order, which is consistent with the decrease in average prediction error.
publishDate 2012
dc.date.none.fl_str_mv 2012-12
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/11336/106476
Allegrini, Franco; Olivieri, Alejandro Cesar; Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization; American Chemical Society; Analytical Chemistry; 84; 24; 12-2012; 10823-10830
0003-2700
CONICET Digital
CONICET
url http://hdl.handle.net/11336/106476
identifier_str_mv Allegrini, Franco; Olivieri, Alejandro Cesar; Analytical Figures of Merit for Partial Least-Squares Coupled to Residual Multilinearization; American Chemical Society; Analytical Chemistry; 84; 24; 12-2012; 10823-10830
0003-2700
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/reference/url/https://pubs.acs.org/doi/suppl/10.1021/ac302996d/suppl_file/ac302996d_si_001.pdf
info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/ac302996d
info:eu-repo/semantics/altIdentifier/doi/10.1021/ac302996d
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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 13.22299

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