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Data representation and feature selection for colorimetric sensor arrays used as explosives detectors

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Authors:
  • Alstrøm, Tommy Sonne ;
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    Orcid logo0000-0003-0941-3146
    Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Larsen, Jan ;
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    Orcid logo0000-0003-1880-1810
    Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Kostesha, Natalie ;
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    Department of Micro- and Nanotechnology, Technical University of Denmark
  • Jakobsen, Mogens Havsteen ;
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    Orcid logo0000-0003-0730-718X
    Department of Micro- and Nanotechnology, Technical University of Denmark
  • Boisen, Anja
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    Orcid logo0000-0002-9918-6567
    Department of Micro- and Nanotechnology, Technical University of Denmark
DOI:
10.1109/MLSP.2011.6064615
Abstract:
Within the framework of the strategic research project Xsense at the Technical University of Denmark, we are developing a colorimetric sensor array which can be useful for detection of explosives like DNT, TNT, HMX, RDX and TATP and identification of volatile organic compounds in the presence of water vapor in air. In order to analyze colorimetric sensors with statistical methods, the sensory output must be put into numerical form suitable for analysis. We present new ways of extracting features from a colorimetric sensor and determine the quality and robustness of these features using machine learning classifiers. Sensors, and in particular explosive sensors, must not only be able to classify explosives, they must also be able to measure the certainty of the classifier regarding the decision it has made. This means there is a need for classifiers that not only give a decision, but also give a posterior probability about the decision. We will compare K-nearest neighbor, artificial neural networks and sparse logistic regression for colorimetric sensor data analysis. Using the sparse solutions we perform feature selection and feature ranking and compare to Gram-Schmidt orthogonalization.
ISBN:
9781457716225, 9781457716218
Type:
Conference paper
Language:
English
Published in:
Machine Learning for Signal Processing, Ieee Workshop on, 2011
Main Research Area:
Science/technology
Publication Status:
Published
Series:
Machine Learning for Signal Processing
Review type:
Peer Review
Conference:
2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011
Publisher:
IEEE
Submission year:
2011
Scientific Level:
Scientific
ID:
233921426

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