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

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EarlyView Article

  • Published: Aug 17, 2017
  • Author: John H. Kalivas, Joan Ferré, Alister J. Tencate

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.

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