Autor segons l'article: Kalivas, John H.; Ferre, Joan; Tencate, Alister J.;
Departament: Química Analítica i Química Orgànica
Autor/s de la URV: Ferré Baldrich, Joan
Paraules clau: Hashtag Etiqueta «#» @uroweb @residentesaeu @infoAeu
Resum: Two popular calibration strategies are classical least squares (CLS) and inverse least squares (ILS). Underlying CLS is that the net analyte signal used for quantitation is orthogonal to signal from other components (interferents). The CLS orthogonality avoids analyte prediction bias from modeled interferents. Although this orthogonality condition ensures full analyte selectivity, it may increase the mean squared error of prediction. Under certain circumstances, it can be beneficial to relax the CLS orthogonality requisite allowing a small interferent bias if, in return, there is a mean squared error of prediction reduction. The bias magnitude introduced by an interferent for a relaxed model depends on analyte and interferent concentrations in conjunction with analyte and interferent model sensitivities. Presented in this paper is relaxed CLS (rCLS) allowing flexibility in the CLS orthogonality constraints. While ILS models do not inherently maintain orthogonality, also presented is relaxed ILS. From development of rCLS, presented is a significant expansion of the univariate selectivity coefficient definition broadly used in analytical chemistry. The defined selectivity coefficient is applicable to univariate and multivariate CLS and ILS calibrations. As with the univariate selectivity coefficient, the multivariate expression characterizes the bias introduced in a particular sample prediction because of interferent concentrations relative to model sensitivities. Specifically, it answers the question of when can a prediction be made for a sample even though the analyte selectivity is poor? Also introduced are new component-wise selectivity and sensitivity measures. Trends in several rCLS figures of merit are characterized for a near infrared data set.
Àrees temàtiques: Statistics & probability Química Mathematics, interdisciplinary applications Matemática / probabilidade e estatística Interdisciplinar Instruments & instrumentation Engenharias iv Engenharias iii Engenharias ii Computer science, artificial intelligence Ciências agrárias i Ciência da computação Chemistry, analytical Biotecnología Biodiversidade Automation & control systems Astronomia / física Applied mathematics Analytical chemistry
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 08869383
Adreça de correu electrònic de l'autor: joan.ferre@urv.cat
Identificador de l'autor: 0000-0001-6240-413X
Data d'alta del registre: 2023-02-18
Volum de revista: 31
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://onlinelibrary.wiley.com/doi/abs/10.1002/cem.2925
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Journal Of Chemometrics. 31 (11):
Referència de l'ítem segons les normes APA: Kalivas, John H.; Ferre, Joan; Tencate, Alister J.; (2017). Selectivity-relaxed classical and inverse least squares calibration and selectivity measures with a unified selectivity coefficient. Journal Of Chemometrics, 31(11), -. DOI: 10.1002/cem.2925
DOI de l'article: 10.1002/cem.2925
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2017
Pàgina inicial: Article number 2925
Tipus de publicació: Journal Publications
Repositori URV