Neural network analysis of chemical compounds in nonrebreathing Fisher-344 rat breath

Robert E. Sackett; Steven K. Rogers; Martin P. DeSimio; James H. Raymer; Dennis W. Ruck; Matthew Kabrisky; Charles A. Bleckmann

doi:10.1117/12.235928

22 March 1996 Neural network analysis of chemical compounds in nonrebreathing Fisher-344 rat breath

Robert E. Sackett, Steven K. Rogers, Martin P. DeSimio, James H. Raymer, Dennis W. Ruck, Matthew Kabrisky, Charles A. Bleckmann

Author Affiliations +

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235928
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

This research applies statistical and artificial neural network analysis to data obtained from measurement of organic compounds in the breath of Fisher-344 rats. The Research Triangle Institute (RTI) developed a breath collection system for use with rates in order to collect and measure volatile organic compounds (VOCs) exhaled. The RTI study tested the hypothesis that VOCs, including endogenous compounds, in breath can serve as markers to exposure to various chemicals such as drugs, pesticides, or carcinogens. A comparative analysis of chromatograms showed that the administration of carbon tetrachloride dramatically altered the VOCs measured in breath; both the compounds detected and their concentrations were greatly impacted. This research demonstrated that neural network analysis and classification discriminates between exposure to carbon tetrachloride versus no exposure. It also identified the chemical compounds in rat breath that best discriminate between carbon tetrachloride exposure and either a vehicle control or no dose. For the data set analyzed, 100 percent classification accuracy was achieved in classifying cases of exposure versus no exposure. The top three marker compounds were identified. The results obtained show that neural networks can be effectively used to analyze complex chromatographic data.

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Robert E. Sackett, Steven K. Rogers, Martin P. DeSimio, James H. Raymer, Dennis W. Ruck, Matthew Kabrisky, and Charles A. Bleckmann "Neural network analysis of chemical compounds in nonrebreathing Fisher-344 rat breath", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235928

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KEYWORDS

Carbon

Virtual colonoscopy

Chemical analysis

Error analysis

Chemical compounds

Analytical research

Neural networks

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