Weighted partial least squares method to improve calibration precision for spectroscopic noiselimited data
Abstract
Multivariate calibration methods have been applied extensively to the quantitative analysis of Fourier transform infrared (FTIR) spectral data. Partial least squares (PLS) methods have become the most widely used multivariate method for quantitative spectroscopic analyses. Most often these methods are limited by model error or the accuracy or precision of the reference methods. However, in some cases, the precision of the quantitative analysis is limited by the noise in the spectroscopic signal. In these situations, the precision of the PLS calibrations and predictions can be improved by the incorporation of weighting in the PLS algorithm. If the spectral noise of the system is known (e.g., in the case of detectornoiselimited cases), then appropriate weighting can be incorporated into the multivariate spectral calibrations and predictions. A weighted PLS (WPLS) algorithm was developed to improve the precision of the analysis in the case of spectralnoiselimited data. This new PLS algorithm was then tested with real and simulated data, and the results compared with the unweighted PLS algorithm. Using nearinfrared (NIR) calibration precision when the WPLS algorithm was applied. The best WPLS method improved prediction precision for the analysis of one of the minor components by a factor of nearly 9 relative tomore »
 Authors:
 Publication Date:
 Research Org.:
 Sandia National Labs., Albuquerque, NM (United States)
 Sponsoring Org.:
 USDOE, Washington, DC (United States)
 OSTI Identifier:
 531089
 Report Number(s):
 SAND972226C; CONF9708121
ON: DE97009353; TRN: 97:005281
 DOE Contract Number:
 AC0494AL85000
 Resource Type:
 Conference
 Resource Relation:
 Conference: 11. international conference on Fourier transform spectroscopy, Athens, GA (United States), 1015 Aug 1997; Other Information: PBD: 1997
 Country of Publication:
 United States
 Language:
 English
 Subject:
 44 INSTRUMENTATION, INCLUDING NUCLEAR AND PARTICLE DETECTORS; 66 PHYSICS; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; INFRARED SPECTRA; MULTIVARIATE ANALYSIS; QUANTITATIVE CHEMICAL ANALYSIS; LEAST SQUARE FIT; FOURIER TRANSFORM SPECTROMETERS; INFRARED SPECTROMETERS; CALIBRATION
Citation Formats
Haaland, D.M., and Jones, H.D.T. Weighted partial least squares method to improve calibration precision for spectroscopic noiselimited data. United States: N. p., 1997.
Web.
Haaland, D.M., & Jones, H.D.T. Weighted partial least squares method to improve calibration precision for spectroscopic noiselimited data. United States.
Haaland, D.M., and Jones, H.D.T. 1997.
"Weighted partial least squares method to improve calibration precision for spectroscopic noiselimited data". United States.
doi:. https://www.osti.gov/servlets/purl/531089.
@article{osti_531089,
title = {Weighted partial least squares method to improve calibration precision for spectroscopic noiselimited data},
author = {Haaland, D.M. and Jones, H.D.T.},
abstractNote = {Multivariate calibration methods have been applied extensively to the quantitative analysis of Fourier transform infrared (FTIR) spectral data. Partial least squares (PLS) methods have become the most widely used multivariate method for quantitative spectroscopic analyses. Most often these methods are limited by model error or the accuracy or precision of the reference methods. However, in some cases, the precision of the quantitative analysis is limited by the noise in the spectroscopic signal. In these situations, the precision of the PLS calibrations and predictions can be improved by the incorporation of weighting in the PLS algorithm. If the spectral noise of the system is known (e.g., in the case of detectornoiselimited cases), then appropriate weighting can be incorporated into the multivariate spectral calibrations and predictions. A weighted PLS (WPLS) algorithm was developed to improve the precision of the analysis in the case of spectralnoiselimited data. This new PLS algorithm was then tested with real and simulated data, and the results compared with the unweighted PLS algorithm. Using nearinfrared (NIR) calibration precision when the WPLS algorithm was applied. The best WPLS method improved prediction precision for the analysis of one of the minor components by a factor of nearly 9 relative to the unweighted PLS algorithm.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 1997,
month = 9
}

Multivariate calibration methods have been applied extensively to the quantitative analysis of Fourier transform infrared (FTIR) spectral data. Partial least squares (PLS) methods have become the most widely used multivariate method for quantitative spectroscopic analyses. Most often these methods are limited by model error or the accuracy or precision of the reference methods. However, in some cases, the precision of the quantitative analysis is limited by the noise in the spectroscopic signal. In these situations, the precision of the PLS calibrations and predictions can be improved by the incorporation of weighting in the PLS algorithm. If the spectral noise ofmore »

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