Exploration of new multivariate spectral calibration algorithms. Page: 3 of 40
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SAND2004 - 1053
Printed March 2004
EXPLORATION OF NEW MULTIVARIATE SPECTRAL CALIBRATION
David M. Haaland, David K. Melgaard, Christine M. Wehlburg, Laura E. Martin, and
Mark H. Van Benthem
Sandia National Laboratories
Albuquerque, NM 87185-0886
A variety of multivariate calibration algorithms for quantitative spectral analyses
were investigated and compared, and new algorithms were developed in the course of this
Laboratory Directed Research and Development project. We were able to demonstrate
the ability of the hybrid classical least squares/partial least squares (CLS/PLS) calibration
algorithms to maintain calibrations in the presence of spectrometer drift and to transfer
calibrations between spectrometers from the same or different manufacturers. These
methods were found to be as good or better in prediction ability as the commonly used
partial least squares (PLS) method. We also present the theory for an entirely new class
of algorithms labeled augmented classical least squares (ACLS) methods. New factor
selection methods are developed and described for the ACLS algorithms. These factor
selection methods are demonstrated using near-infrared spectra collected from a system
of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved
ease of use and better prediction ability than PLS when transferring calibrations between
near-infrared calibrations from the same manufacturer. Finally, simulations
incorporating either ideal or realistic errors in the spectra were used to compare the
prediction abilities of the new ACLS algorithm with that of PLS. We found that in the
presence of realistic errors with non-uniform spectral error variance across spectral
channels or with spectral errors correlated between frequency channels, ACLS methods
generally out-performed the more commonly used PLS method. These results
demonstrate the need for realistic error structure in simulations when the prediction
abilities of various algorithms are compared. The combination of equal or superior
prediction ability and the ease of use of the ACLS algorithms make the new ACLS
methods the preferred algorithms to use for multivariate spectral calibrations.
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Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J. (The Dow Chemical Company, Midland, MI) et al. Exploration of new multivariate spectral calibration algorithms., report, March 1, 2004; United States. (digital.library.unt.edu/ark:/67531/metadc883329/m1/3/: accessed December 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.