Record Display for the EPA National Library Catalog

RECORD NUMBER: 11 OF 24

OLS Field Name OLS Field Data
Main Title Pattern Recognition Studies of Complex Chromatographic Data Sets.
Author Jurs, P. C. ; Lavine, B. K. ; Stouch, T. R. ;
CORP Author Pennsylvania State Univ., University Park.;Health Effects Research Lab., Research Triangle Park, NC.
Year Published 1985
Report Number EPA-R-811037; EPA/600/J-85/386;
Stock Number PB86-197167
Additional Subjects Chromatographic analysis ; Pattern recognition ; Chemical analysis ; Calibrating ; Reaction kinetics ; Mathematical models ; Spectroscopic analysis ; Crystallography ; Random walk ; Polymers ; Comparison ; Experimental design ; Electrochemistry ; Reprints ; Kalman filtering ; Procedures ; Chemometrics
Holdings
Library Call Number Additional Info Location Last
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Status
NTIS  PB86-197167 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 06/21/1988
Collation 9p
Abstract
Chromatographic fingerprinting of complex biological samples is an active research area with a large and growing literature. Multivariate statistical and pattern recognition techniques can be effective methods for the analysis of such complex data. However, the classification of complex samples on the basis of their chromatographic profiles is complicated by two factors: (1) confounding of the desired group information by experimental variables or other systematic variations, and (2) random or chance classification effects with linear discriminants. The report focuses on several current projects involving these effects and methods for dealing with the effects. Complex chromatographic data sets often contain information dependent on experimental variables as well as information which differentiates between classes. The existence of these types of complicating relationships is an innate part of fingerprint-type data. ADAPT, an interactive computer software system, has the clustering, mapping, and statistical tools necessary to identify and study these effects in realistically large data sets. In one study, pattern recognition analysis of 144 pyrochromatograms (PyGCs) from cultured skin fibroblasts was used to differentiate cystic fibrosis carriers from presumed normal donors. In another study, gas chromatographic profiles of cuticular hydrocarbon extracts obtained from 179 fire ants were analyzed using pattern recognition methods to seek relations with social caste and colony. Confounding relationships were studied by logistic regression. The data analysis techniques used in these two example studies will be presented.