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Neural network recognition of chemical class information in mobility spectra obtained at high temperaturesA minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.
Document ID
20040141626
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Bell, S.
(Eastern Washington University Cheney, Washington 99004-2431, United States)
Nazarov, E.
Wang, Y. F.
Rodriguez, J. E.
Eiceman, G. A.
Date Acquired
August 22, 2013
Publication Date
March 15, 2000
Publication Information
Publication: Analytical chemistry
Volume: 72
Issue: 6
ISSN: 0003-2700
Subject Category
Life Sciences (General)
Funding Number(s)
CONTRACT_GRANT: NAGY4-558
CONTRACT_GRANT: DAAH04-95-1-0541
Distribution Limits
Public
Copyright
Other
Keywords
NASA Discipline Environmental Health
Non-NASA Center

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