Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

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 fullyintegrated 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 traf- ficking of illegal drugs, explosive detection, or in other law enforcement applications.EU FP7 Grant Agreement Number 31320

Sound <i>f</i>_<i>o</i> ranges for different call types and laryngeal performance in vitro in <i>Myotis daubentonii</i>.

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Oscillation onset of vocal membranes requires fast pressure modulation compared to ventricular folds.

(a) Sound spectrograms during slow 1 kPa/s bronchial pressure show that vocal membranes did not oscillate during slow ramps (in 3 out of 4 individuals). (b) Driven by fast pressure modulation, vocal membranes reliably vibrated. Red vertical dashed lines show the onset for PTP detection. The data underlying a and b can be found in S4 and S5 Data files.</p

The vocal range of laryngeal structures in vitro corresponds to frequency ranges of distinct social calls in Daubenton’s bat.

(a) Vocal membrane fo range in vitro (blue vertical bar) compared to reported in vivo range for social [8] and echolocation calls [24] of M. daubentonii. (b) Ventricular fold fo range in vitro (green vertical bar) corresponds well to fo range of agonistic social calls of M. daubentonii. Boxplot whiskers indicate range. For values see S2 Table. Inset shows a spectrogram and oscillogram of an agonistic social call. Abbreviations as in Fig 1. The data underlying a and b can be found in S6 Data.</p