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Title: Exploration of new multivariate spectral calibration algorithms.

Abstract

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 (CLSIPLS) 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 presencemore » 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.« less

Authors:
; ; ; ; ;  [1];  [2]
  1. The Dow Chemical Company, Midland, MI
  2. Merck & Co. Inc., West Point, PA
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
918755
Report Number(s):
SAND2004-1053
TRN: US200825%%14
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; MULTIVARIATE ANALYSIS; CALIBRATION; SPECTROMETERS; LEAST SQUARE FIT; INFRARED SPECTRA; Error analysis (Mathematics); Spectral analysis-Instruments; Algorithms.; Simulation methods.; Calibration.; Multivariate analysis.

Citation Formats

Van Benthem, Mark Hilary, Haaland, David Michael, Melgaard, David Kennett, Martin, Laura Elizabeth, Wehlburg, Christine Marie, Pell, Randy J, and Guenard, Robert D. Exploration of new multivariate spectral calibration algorithms.. United States: N. p., 2004. Web. doi:10.2172/918755.
Van Benthem, Mark Hilary, Haaland, David Michael, Melgaard, David Kennett, Martin, Laura Elizabeth, Wehlburg, Christine Marie, Pell, Randy J, & Guenard, Robert D. Exploration of new multivariate spectral calibration algorithms.. United States. doi:10.2172/918755.
Van Benthem, Mark Hilary, Haaland, David Michael, Melgaard, David Kennett, Martin, Laura Elizabeth, Wehlburg, Christine Marie, Pell, Randy J, and Guenard, Robert D. Mon . "Exploration of new multivariate spectral calibration algorithms.". United States. doi:10.2172/918755. https://www.osti.gov/servlets/purl/918755.
@article{osti_918755,
title = {Exploration of new multivariate spectral calibration algorithms.},
author = {Van Benthem, Mark Hilary and Haaland, David Michael and Melgaard, David Kennett and Martin, Laura Elizabeth and Wehlburg, Christine Marie and Pell, Randy J and Guenard, Robert D},
abstractNote = {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 (CLSIPLS) 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.},
doi = {10.2172/918755},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2004},
month = {3}
}

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