Analysis of IMS spectra using neural networks
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; USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 7109966
- 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
- Country of Publication:
- United States
- Language:
- English
Similar Records
Voltron Compatible Whole Building Root-Fault Detection and Diagnosis
Neural network and letter recognition
Related Subjects
ORGANIC
PHYSICAL AND ANALYTICAL CHEMISTRY
ION MOBILITY
SPECTROSCOPY
ORGANIC COMPOUNDS
GAS ANALYSIS
DATA ANALYSIS
VOLATILE MATTER
ALCOHOLS
ALDEHYDES
AROMATICS
DATA PROCESSING
ESTERS
EXPERT SYSTEMS
GAS CHROMATOGRAPHY
KETONES
LEARNING
NEURAL NETWORKS
PATTERN RECOGNITION
PEAKS
TRAINING
VAPORS
CHROMATOGRAPHY
EDUCATION
FLUIDS
GASES
HYDROXY COMPOUNDS
MATTER
MOBILITY
PARTICLE MOBILITY
PROCESSING
SEPARATION PROCESSES
400201* - Chemical & Physicochemical Properties