@CONFERENCE\{IMM2017-06983,
    author       = "L. L. M{\o}lgaard and O. Buus and J. Larsen and H. Babamoradi and I. L. Thygesen and M. Laustsen and J. K. Munk and E. Dossi and C. {O'}Keeffe and L. L{\"{a}}ssig and S. Tatlow and L. Sandstr{\"{o}}m and M. H. Jakobsen",
    title        = "Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning",
    year         = "2017",
    month        = "apr",
    booktitle    = "Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing {XVIII}",
    volume       = "10183",
    series       = "",
    editor       = "",
    publisher    = "SPIE",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6983-full.html",
    abstract     = "We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling.
The sensing technology has been developed in the context of the {CRIM-TRACK} project. At present a fully-integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios.
To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically
identifying unreliable measurements that could produce false predictions.
The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios.
The technology under development in {CRIM-TRACK} has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications."
}