Published 2010

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Publication details

Journal : Journal of Chemometrics , vol. 24 , p. 288–299–12 , 2010

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

Publication type : Academic article

Contributors : Shi, Zhenqi; Cogdill, Robert P; Martens, Harald; Anderson, Carl A

Issue : 5

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Kjetil Aune
Chief Librarian


The time and expense of calibration development limit the feasibility of NIR spectroscopy for many industrial applications, with a major portion of the costs being related to creation of a sufficient set of calibration samples. Net analyte signal (NAS) and generalized least squares (GLS) pre-processing have been proposed in the literature as methods to simplify multivariate calibration by reducing the quantity of calibration samples by orthogonalizing or shrinking interference signals. Synthetic calibration has also been reported as a method to combine interference signals with pure component spectra to generate virtual calibration models, thereby reducing the number of real calibration samples required. The goals of this paper were to (1) compare theoretical and practical differences between NAS and GLS pre-processing and (2) explore the potential of simplified NIR calibrations, both empirical and synthetic, constructed using optical coefficient-based signal processing on predicting chemical compositions of pharmaceutical powder mixtures. A reduced calibration dataset including only one pharmaceutical powder mixture composition and pure component spectra was used for both empirical and synthetic calibrations. Absorption and reduced scattering coefficients, obtained from spatially-resolved spectroscopy, were used herein as interference signals in NAS/GLS pre-processing for both calibrations. As a result, NAS and GLS were shown to be equivalent in both theoretical and practical senses. After optical coefficient-based signal processing, simplified calibrations, both empirical and synthetic, were demonstrated to have similar model performance as generic pre-processing methods such as SNV and derivative, while requiring fewer principal components and achieving a lower prediction error. Copyright (C) 2010 John Wiley & Sons, Ltd.