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Title: Method for factor analysis of GC/MS data

Abstract

The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.

Inventors:
; ;
Issue Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1078320
Patent Number(s):
8266197
Application Number:
12/754,041
Assignee:
Sandia Corporation (Albuquerque, NM)
Patent Classifications (CPCs):
G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Van Benthem, Mark H, Kotula, Paul G, and Keenan, Michael R. Method for factor analysis of GC/MS data. United States: N. p., 2012. Web.
Van Benthem, Mark H, Kotula, Paul G, & Keenan, Michael R. Method for factor analysis of GC/MS data. United States.
Van Benthem, Mark H, Kotula, Paul G, and Keenan, Michael R. Tue . "Method for factor analysis of GC/MS data". United States. https://www.osti.gov/servlets/purl/1078320.
@article{osti_1078320,
title = {Method for factor analysis of GC/MS data},
author = {Van Benthem, Mark H and Kotula, Paul G and Keenan, Michael R},
abstractNote = {The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {9}
}

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Works referenced in this record:

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