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Title: Hybrid least squares multivariate spectral analysis methods

Patent ·
OSTI ID:874212
 [1]
  1. Albuquerque, NM
+ Show Author Affiliations

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

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 6341257
OSTI ID:
874212
Country of Publication:
United States
Language:
English

References (14)

Methods to Include Beer's Law Nonlinearities in Quantitative Spectral Analysis book January 1987
Maximum Likelihood Multivariate Calibration journal July 1997
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 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
Partial least-squares methods for spectral analyses. 2. Application to simulated and glass spectral data journal June 1988

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Related Subjects

hybrid
squares
multivariate
spectral
analysis
methods
set
shapes
components
effects
original
calibration
step
added
following
estimation
improve
accuracy
amount
sampled
mixture
method
means
combination
initial
classical
subsequent
inverse
shape
normally
non-calibrated
chemical
component
sample
mean
sources
variation
including
temperature
drift
shifts
spectrometers
spectrometer
etc
continuous
discontinuous
discrete
illustrative
effect
analysis method
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