Separation of merged mass-spectral patterns by feed-forward neural network filtering

Thomas G. Thomas; Dennis G. Smith

doi:10.1117/12.460277

15 November 2002 Separation of merged mass spectral patterns by feed-forward neural network filtering

Thomas G. Thomas, Dennis G. Smith

Proceedings Volume 4788, Photonic Devices and Algorithms for Computing IV; (2002) https://doi.org/10.1117/12.460277
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

This paper describes the separation of merged signals from a mass-selective chromatographic detector by means of an adaptive filtering technique. The technique is based on parallel feed-forward neural networks, which are trained to resolve the mass spectra of two merged chemical compounds. Specifically, the chemical mass spectra of the compounds ethyl benzene and xylene were used to evaluate a filter based on probabilistic neural networks (PNN). The results are that the PNN filter shows good noise rejection and is fast enough computationally to be utilized in real time. The filter technique has applications in on-line processing of environmental monitoring instrumentation data and direct processing of pixel spectral data, such as hyperspectral image cubes.

Citation Download Citation

Thomas G. Thomas and Dennis G. Smith "Separation of merged mass spectral patterns by feed-forward neural network filtering", Proc. SPIE 4788, Photonic Devices and Algorithms for Computing IV, (15 November 2002); https://doi.org/10.1117/12.460277

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