Chemotaxonomic Characterization of Microorganisms by Capillary Gas Chromatography-Mass Spectrometry

Abstract

Research is an interdisciplinary basic research program in biodetection using in mass spectrometry. Identification of specific pyrolytic chemical markers for microorganisms that are capable of identifying important biological threats is essential to the success of mass spectrometer-based systems for biodetection. Results from the first model system to data have demonstrated clearly that decisive information for bacterial identification can be generated by analytical pyrolysis using chemical markers. Derivation GC of bacterial samples may be simplified by the choice of selective chemical reactions and sample cleanup steps which remove many contaminating or interfering components. A network of HP GC-MS data stations enables the interchange of data files between any two machines. A number of programs have been developed for data display and pattern recognition of multivariate pyrolysis data. Currently the package performs data pretreatment and feature selection, principal component analysis, hierarchical single linkage clustering, and nonlinear mapping. The capabilities have been extended to include linear, quadratic, and stepwise discriminant analysis and other heuristic factor analysis approaches. Keywords: Capillary gas chromatography, Biodetection, Mass spectrometry.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1988
Accession Number
ADA198251

Entities

People

  • Alvin Fox
  • Stephen L. Morgan

Organizations

  • University of South Carolina

Tags

DTIC Thesaurus Topics

  • Amino Sugars
  • Chemical Reactions
  • Chemistry
  • Data Analysis
  • Factor Analysis
  • Feature Selection
  • Identification
  • Instrumentation
  • Mass Spectrometers
  • Mass Spectrometry
  • Microbiology
  • Microorganisms
  • Pattern Recognition
  • Recognition
  • Spectrometers
  • Spectrometry
  • Spectroscopy

Fields of Study

  • Chemistry

Readers

  • Analytical Chemistry
  • Computer Science.
  • Computer Vision.

Technology Areas

  • AI & ML