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.
A program for automated waste mixture identification (PAW-MI) was developed as a preliminary screening tool for unknown organics handled on remedial action sites. PAWMI has three subroutines: the Interferogram processing and peak selection subroutine (PUSHSUB), the Interpretation subroutine (PAIRS), and final processing subroutine to subtract spectral similarity (PAIRSPLUS). The method can be used to Interpret
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