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Selectivity-relaxed classical and inverse least squares calibration and selectivity measures with a unified selectivity coefficient

  • Datos identificativos

    Identificador: imarina:5131701
    Autores:
    Kalivas, John H.Ferre, JoanTencate, Alister J.
    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.
  • Otros:

    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: No poverty
    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: 2024-11-16
    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: https://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
  • Palabras clave:

    Analytical Chemistry,Applied Mathematics,Automation & Control Systems,Chemistry, Analytical,Computer Science, Artificial Intelligence,Instruments & Instrumentation,Mathematics, Interdisciplinary Applications,Statistics & Probability
    No poverty
    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
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