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Title: Analysis of IMS spectra using neural networks

Conference ·
OSTI ID:10171157

Ion mobility spectrometry (IMS) has been used for over 20 years, and IMS coupled to gas chromatography (GC/IMS) has been used for over 10 years. There still is no systematic approach to IMS spectral interpretation such as exists for mass spectrometry and infrared spectrometry. Neural networks, a form of adaptive pattern recognition, were examined as a method of data reduction for IMS and GC/IMS. A wide variety of volatile organics were analyzed using IMS and GC/IMS and submitted to different networks for identification. Several different networks and data preprocessing algorithms were studied. A network was linked to a simple rule-based expert system and analyzed. The expert system was used to filter out false positive identifications made by the network using retention indices. The various network configurations were compared to other pattern recognition techniques, including human experts. The network performance was comparable to human experts, but responded much faster. Preliminary comparison of the network to other pattern recognition showed comparable performance. Linkage of the network output to the rule-based retention index system yielded the best performance.

Research Organization:
Los Alamos National Lab., NM (United States)
Sponsoring Organization:
USDOE, Washington, DC (United States)
DOE Contract Number:
W-7405-ENG-36
OSTI ID:
10171157
Report Number(s):
LA-UR-92-1953; CONF-9206243-2; ON: DE92017511
Resource Relation:
Conference: Workshop on ion mobility spectrometry,Ruidoso, NM (United States),22-26 Jun 1992; Other Information: PBD: [1992]
Country of Publication:
United States
Language:
English