Autor según el artículo: Kalivas, John H.; Ferre, Joan; Tencate, Alister J.;

Departamento: Química Analítica i Química Orgànica

Autor/es de la URV: Ferré Baldrich, Joan

Palabras clave: Hashtag Etiqueta «#» @uroweb @residentesaeu @infoAeu

Resumen: 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.

Áreas temáticas: 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

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 08869383

Direcció de correo del autor: joan.ferre@urv.cat

Identificador del autor: 0000-0001-6240-413X

Fecha de alta del registro: 2023-02-18

Volumen de revista: 31

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://onlinelibrary.wiley.com/doi/abs/10.1002/cem.2925

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Journal Of Chemometrics. 31 (11):

Referencia 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 del artículo: 10.1002/cem.2925

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2017

Página inicial: Article number 2925

Tipo de publicación: Journal Publications