Signal processing for GC-MS measurements for biomarker identification

Marina D' Angelo, Technische Universiteit Eindhoven (TUE). Stan Ackermans Instituut. Design and Technology of Instrumentation (DTI)

    Research output: ThesisPd Eng Thesis

    260 Downloads (Pure)

    Abstract

    Breath analysis is a technique that is gaining importance in both industry and academia. Potentially, it is a non-invasive technique that will allow screening, diagnosing and monitoring of patients.
    Many studies have been performed in an attempt to make a distinction between healthy and sick patients by only studying their breath. It was proven successful for detecting lung and breast cancer, for identifying transplant rejection and for diagnosing liver disease among others.
    Ideally, it is Philips goal to develop a device that can take breath, process it and classify the patient as healthy or sick. Initially, this would be done for respiratory diseases, including asthma and sepsis with respiratory complications.
    However, processing breath is not simple. There is a spectrum of possible devices for analysis.
    Electronic noses are a great bedside alternative, while gas chromatography is ideal for research studies were the nature of the biomarkers should be found.
    Philips is involved in several studies within the next couple of years, for asthma and sepsis among others, and will process the samples with gas chromatography-mass spectrometry (GC-MS).
    My objective was to provide a reliable software workflow for the analysis of the very complex GC-MS data, which could identify the molecules present in them and provide a reliable list of possible biomarkers as an output. This list would in the future be used to train classifiers for the mentioned diseases.
    The result of this project was a complete processing workflow, beginning with the use of a third party peak extraction software, followed by the customized design of a filtering and alignment solution.
    This combination provides a highly sensitive compound detection algorithm, a reliable peak quality filter and an accurate solution for comparison of multiple samples. Results are provided in a flexible manner for comprising a variety of classifier design possibilities.
    This solution can greatly contribute to the analysis GC-MS data for biomarker identification.
    Original languageEnglish
    Awarding Institution
    Supervisors/Advisors
    • Nijsen, Tamara, Supervisor
    • Vink, A., External supervisor, External person
    • de Jong, Arthur M., Supervisor
    Award date1 Dec 2011
    Place of PublicationEindhoven
    Publisher
    Print ISBNs978-90-444-1086-0
    Publication statusPublished - 2011

    Bibliographical note

    Eindverslag.

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