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Published 2021

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Publication details

Journal : Journal of Chemometrics , 2021

International Standard Numbers :
Printed : 0886-9383
Electronic : 1099-128X

Publication type : Academic article

Contributors : Eskildsen, Carl Emil Aae; Engelsen, Søren B.; Dankel, Elin Katinka Riiser; Solberg, Lars Erik; Næs, Tormod

Summary

Problems concerning covariance among independent variables are well
understood and dealt with by inverse regression methods like partial least
squares regression. However, covariance between dependent variables has only
received minor attention. Biological samples are often complex mixtures of
multiple covarying compounds. During multivariate calibration, analyte predictions
may be mediated through relationships with interfering compounds,
which implies that the calibration model is not providing a direct link between
the multivariate measurements and the analyte of interest. This compromises
robustness and validity of the calibration model—important aspects when
applying the model to future samples and data sets. This study discusses issues
of calibration modeling when strong covariance structures exist among the
analyte of interest and interfering compounds.
We propose a projection-based method to diagnose whether indirect covariance
structures dominate the calibration model. The proposed method is tested
on a two-constituent Beer's law system consisting of 20 aqueous samples with
covarying amounts of fructose (analyte of interest) and riboflavin (interfering
compound). Transmission measurements are obtained on all samples in the
visual and near-infrared wavelength ranges. Riboflavin has strong absorption
in the visual region, whereas fructose exclusively absorbs in the near-infrared
region. Hence, predictions of fructose concentrations, obtained from the visual
wavelength range only, are fully mediated through riboflavin, whereas fructose
predictions obtained from the near-infrared wavelength range may be obtained
independent of riboflavin.

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