Differential Profiling of Volatile Organic Compound Biomarker Signatures Utilizing a Logical Statistical Filter-Set and Novel Hybrid Evolutionary Classifiers

Abstract

Volatile organic compounds (VOCs) can be monitored to reveal the identity of a unique individual, as well their physiological status. Given the analysis requirements for differential profiling via gas chromatography/mass spectrometry, our group has developed a novel informatics platform, Metabolite Differentiation and Discovery Lab (MeDDL). MeDDL's toolset identifies candidate VOCs to be used for classification. A K-nearest neighbor classifier and genetic algorithm (GA) are used to optimize the classifier and subset of VOCs. The GA uses the area the ROC curve as the optimization measure. Very promising results have been obtained on over a dozen odor recognition problems.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2012
Accession Number
ADA562341

Entities

People

  • Claude C. Grigsby
  • Derek W. Boone
  • Mateen M. Rizki
  • Michael A. Zmuda
  • Ryan M. Kramer
  • Tyler C. Highlander

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Biological Markers
  • Chromatography
  • Classification
  • Computer Programs
  • Computer Science
  • Gas Chromatography
  • Genetic Algorithms
  • Identification
  • Information Science
  • Machine Learning
  • Mass Spectrometry
  • Organic Compounds
  • Spectrometry
  • Volatile Organic Compounds

Readers

  • Analytical Chemistry
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology