A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression

Applied Spectroscopy
Stephen R Delwiche, James B Reeves

Abstract

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial com...Continue Reading

References

Jan 1, 1968·Applied Optics·F R Stauffer, H Sakai
Sep 1, 1972·Analytical Chemistry·J SteinierJ Deltour

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Citations

Dec 17, 2014·Applied Spectroscopy·Mirosław A Czarnecki
Jan 29, 2016·Annals of Biomedical Engineering·Cushla M McGoverinNancy Pleshko
Jul 10, 2016·Journal of Occupational Rehabilitation·Sandra AbegglenHans-Jörg Znoj
Aug 2, 2017·Journal of Comparative Physiology. B, Biochemical, Systemic, and Environmental Physiology·Nadine HavensteinJoanna Fietz

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