title: Spectral peak verification and recognition using a multilayered neural network.
abstract: 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.
authors.affiliation: Department of Chemistry, University of New Hampshire, Durham, 03824.
authors.forename: B J
authors.initials: BJ
authors.lastname: Wythoff
authors.forename: S P
authors.initials: SP
authors.lastname: Levine
authors.forename: S A
authors.initials: SA
authors.lastname: Tomellini
mesh_terms: D001185:Artificial Intelligence; D002623:Chemistry Techniques, Analytical / Q000295:instrumentation*; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology*