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Title: Classical least squares multivariate spectral analysis

Patent ·
OSTI ID:874560
 [1]
  1. Albuquerque, NM
+ Show Author Affiliations

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

Research Organization:
Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
DOE Contract Number:
AC04-94AL85000
Assignee:
Sandia Corporation (Albuquerque, NM)
Patent Number(s):
US 6415233
OSTI ID:
874560
Country of Publication:
United States
Language:
English

References (15)

Methods to Include Beer's Law Nonlinearities in Quantitative Spectral Analysis book January 1987
Comparison of multivariate calibration methods for quantitative spectral analysis journal May 1990
Error propagation and figures of merit for quantification by solving matrix equations journal May 1986
Multivariate Calibration Methods Applied to Quantitative FT-IR Analyses book January 1990
Improved Sensitivity of Infrared Spectroscopy by the Application of Least Squares Methods journal September 1980
Multivariate Least-Squares Methods Applied to the Quantitative Spectral Analysis of Multicomponent Samples journal January 1985
Application of New Least-Squares Methods for the Quantitative Infrared Analysis of Multicomponent Samples journal November 1982
New Prediction-Augmented Classical Least-Squares (PACLS) Methods: Application to Unmodeled Interferents journal September 2000
Enhancing IR Detection Limits for Trace Polar Organics in Aqueous Solutions with Surface-Modified Sol-Gel-Coated ATR Sensors journal April 1999
Partial least-squares method for spectrofluorimetric analysis of mixtures of humic acid and lignin sulfonate journal April 1983
Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information journal June 1988
Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution journal January 1995
Spectral data files for self-modeling curve resolution with examples using the Simplisma approach journal February 1997
Quantitative infrared analysis of borophosphosilicate films using multivariate statistical methods journal June 1988
Partial least-squares methods for spectral analyses. 2. Application to simulated and glass spectral data journal June 1988

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classical
squares
multivariate
spectral
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improved
method
adds
shapes
describing
non-calibrated
components
effects
baseline
corrections
analyzed
mixture
prediction
phase
improvements
decrease
eliminate
restrictions
cls-type
methods
greatly
extend
capabilities
accuracy
precision
application
pacls
ability
accurately
predict
sample
concentrations
unmodeled
samples
applications
incorporation
spectrometer
drift
quantitative
model
maintenance
calibration
drifting
finally
transfer
spectrometers
demonstrated
analysis method
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