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 = {Tue Sep 11 00:00:00 EDT 2012},
month = {Tue Sep 11 00:00:00 EDT 2012}
}
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