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Title: Classical least squares multivariate spectral analysis

Abstract

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.

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:
874560
Patent Number(s):
6415233
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:
classical; squares; multivariate; spectral; analysis; 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; /702/430/

Citation Formats

Haaland, David M. Classical least squares multivariate spectral analysis. United States: N. p., 2002. Web.
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Haaland, David M. Classical least squares multivariate spectral analysis. United States.
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Haaland, David M. Tue . "Classical least squares multivariate spectral analysis". United States. https://www.osti.gov/servlets/purl/874560.
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@article{osti_874560,
title = {Classical least squares multivariate spectral analysis},
author = {Haaland, David M},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2002},
month = {1}
}
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Patent:
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Works referenced in this record:

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

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

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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