Document Detail

Discriminant analysis of fused positive and negative ion mobility spectra using multivariate self-modeling mixture analysis and neural networks.
MedLine Citation:
PMID:  18284787     Owner:  NLM     Status:  PubMed-not-MEDLINE    
A new method coupling multivariate self-modeling mixture analysis and pattern recognition has been developed to identify toxic industrial chemicals using fused positive and negative ion mobility spectra (dual scan spectra). A Smiths lightweight chemical detector (LCD), which can measure positive and negative ion mobility spectra simultaneously, was used to acquire the data. Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) was used to separate the analytical peaks in the ion mobility spectra from the background reactant ion peaks (RIP). The SIMPLSIMA analytical components of the positive and negative ion peaks were combined together in a butterfly representation (i.e., negative spectra are reported with negative drift times and reflected with respect to the ordinate and juxtaposed with the positive ion mobility spectra). Temperature constrained cascade-correlation neural network (TCCCN) models were built to classify the toxic industrial chemicals. Seven common toxic industrial chemicals were used in this project to evaluate the performance of the algorithm. Ten bootstrapped Latin partitions demonstrated that the classification of neural networks using the SIMPLISMA components was statistically better than neural network models trained with fused ion mobility spectra (IMS).
Ping Chen; Peter B Harrington
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Applied spectroscopy     Volume:  62     ISSN:  0003-7028     ISO Abbreviation:  Appl Spectrosc     Publication Date:  2008 Feb 
Date Detail:
Created Date:  2008-02-20     Completed Date:  2008-04-03     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0372406     Medline TA:  Appl Spectrosc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  133-41     Citation Subset:  -    
Ohio University, Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Clippinger Laboratories, Athens, Ohio 45701-2979, USA.
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