Spectral peak verification and recognition using a multilayered neural network

The verification and recognition of peak-shaped signals in analytical data are ubiquitous scientific problems. Experimental data contain overlapping signals and noise, which make sensitive and reliable peak recognition difficult. A peak detection system based on a class of neural networks known as &...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 1990-12, Vol.62 (24), p.2702-2709
Main Authors: Wythoff, Barry J, Levine, Steven P, Tomellini, Sterling A
Format: Article
Language:English
Subjects:
Analytical chemistry
Artificial Intelligence
Chemistry
Chemistry Techniques, Analytical - instrumentation
Computer Simulation
Exact sciences and technology
Humans
Models, Theoretical
Neurons - physiology
Spectrometric and optical methods
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Summary:The verification and recognition of peak-shaped signals in analytical data are ubiquitous scientific problems. Experimental data contain overlapping signals and noise, which make sensitive and reliable peak recognition difficult. A peak detection system based on a class of neural networks known as "multilayered perceptrons" has been created. The network was trained and evaluated with use of vapor-phase infrared spectral data. The results of varying the network architecture on system training and prediction performance along with refinement of the form of the input pattern are presented.
ISSN:0003-2700
1520-6882
DOI:10.1021/ac00223a011