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

  • Identification data

    Identifier: imarina:5131701
    Authors:
    Kalivas, John H.Ferre, JoanTencate, Alister J.
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Kalivas, John H.; Ferre, Joan; Tencate, Alister J.;
    Department: Química Analítica i Química Orgànica
    URV's Author/s: Ferré Baldrich, Joan
    Keywords: No poverty
    Abstract: 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 08869383
    Author's mail: joan.ferre@urv.cat
    Author identifier: 0000-0001-6240-413X
    Record's date: 2024-11-16
    Journal volume: 31
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://onlinelibrary.wiley.com/doi/abs/10.1002/cem.2925
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Journal Of Chemometrics. 31 (11):
    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
    Article's DOI: 10.1002/cem.2925
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2017
    First page: Article number 2925
    Publication Type: Journal Publications
  • Keywords:

    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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