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Spectral peak verification and recognition using a multi-layered neural network.

Authors
Wythoff-B; Levine-SP; Tomellini-SA
Source
School of Public Health, University of Michigan, Ann Arbor, Michigan :1-36
Link
NIOSHTIC No.
00197308
Abstract
The feasibility of exploiting neural network technology to recognize peak shaped signals in analytical data was evaluated. A peak detection system based on a class of neural networks known as multilayered perceptrons was created. While the system described was developed to interpret infrared spectral data, peak detection has implications in every chemical application where the recognition of peak shaped signals in analytical data is important. Chemical applications could include virtually all spectroscopic and chromatographic methods, as well as flow injection analysis and the scanning electrochemical methods. The idea that the trained human and artificial neural network can adequately perform the signal detection task under similar conditions was examined. The incorporation of a noise reference was found to aid both the human and the network signal detection process.
Keywords
NIOSH-Grant; Grants-other; Analytical-methods; Chemical-analysis; Analytical-chemistry
Contact
Environmental & Indust Health School of Public Health II 1420 Washington Heights Ann Arbor, MI 48109-2029
Document Type
Final Grant Report
Funding Amount
238744
Funding Type
Grant
NTIS Accession No.
PB91-173773
NTIS Price
A04
Identifying No.
Grant-Number-R01-OH-02404
NIOSH Division
OEP
Priority Area
Other Occupational Concerns; Grants-other
Source Name
School of Public Health, University of Michigan, Ann Arbor, Michigan
State
MI
Performing Organization
University of Michigan at Ann Arbor, Ann Arbor, Michigan
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