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Proceedings Paper

A neural network identification system for space-borne GCMS pattern recognition
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Paper Abstract

We present experimental results of training a neural network to perform chemical compound identification from a portable space-borne gas chromatographic mass spectrometer (GCMS). The GCMS data has distortion, peak overlap, and noise problems. A signal processing algorithm is first applied to the GCMS to detect the peaks and to clean the MS spectra. We design neural networks to be trained on a sub-set of chemicals that are closely related in the GC graph. Each sub-neural network then identifies the compounds within the sub-set. We design the training data using mostly NIST standard MS data. The NIST mass spectral data of multiple compounds are mixed to train the neural network to identify mixed species. Back-propagation learning algorithm is used to train the neural network. Good identification results have been obtained.

Paper Details

Date Published: 9 April 2007
PDF: 11 pages
Proc. SPIE 6574, Optical Pattern Recognition XVIII, 65740E (9 April 2007); doi: 10.1117/12.723633
Show Author Affiliations
Thomas T. Lu, Jet Propulsion Lab. (United States)
Tien-Hsin Chao, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 6574:
Optical Pattern Recognition XVIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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