Conference paper
Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Copenhagen Center for Health Technology, Centers, Technical University of Denmark3
Department of Micro- and Nanotechnology, Technical University of Denmark4
Surface Engineering, Department of Micro- and Nanotechnology, Technical University of Denmark5
Cranfield University6
Securetec Detektions-Systeme AG7
Pro Design Electronic GmbH8
Gammadata Instrument AB9
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.
Language: | English |
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Publisher: | SPIE - International Society for Optical Engineering |
Year: | 2017 |
Pages: | 1018307-1018307-8 |
Proceedings: | Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVIII |
Series: | Proceedings of Spie - the International Society for Optical Engineering |
ISBN: | 1510608672 , 1510608680 , 9781510608672 and 9781510608689 |
ISSN: | 1996756x and 0277786x |
Types: | Conference paper |
DOI: | 10.1117/12.2262468 |
ORCIDs: | Mølgaard, Lasse Lohilahti , Buus, Ole Thomsen , Larsen, Jan , Thygesen, Ida Lysgaard and Jakobsen, Mogens Havsteen |