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Title: Hybrid least squares multivariate spectral analysis methods

Abstract

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.

Inventors:
 [1]
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  1. Albuquerque, NM
Issue Date:
Research Org.:
Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
OSTI Identifier:
874212
Patent Number(s):
6341257
Assignee:
Sandia Corporation (Albuquerque, NM)
Patent Classifications (CPCs):
G - PHYSICS G01 - MEASURING G01J - MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT
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DOE Contract Number:  
AC04-94AL85000
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
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; /702/703/

Citation Formats

Haaland, David M. Hybrid least squares multivariate spectral analysis methods. United States: N. p., 2002. Web.
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Haaland, David M. Hybrid least squares multivariate spectral analysis methods. United States.
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Haaland, David M. Tue . "Hybrid least squares multivariate spectral analysis methods". United States. https://www.osti.gov/servlets/purl/874212.
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@article{osti_874212,
title = {Hybrid least squares multivariate spectral analysis methods},
author = {Haaland, David M},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2002},
month = {1}
}
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Works referenced in this record:

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

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

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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