8 research outputs found

    Validation of the Draft Community Reference Method for the Determination of Solvent Yellow 124 in Gas Oil (Euromarker)

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    Abstract not availableJRC.D-Institute for Reference Materials and Measurements (Geel

    Production of Two Certified Reference Materials for the Determination of SY 124 (Euromarker) in Gas Oil

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    Two reference materials with certified mass fractions of SY124 in gas oil have been prepared. Samples were prepared by spiking of blank gas oil with pure SY124. Homogeneity and stability were confirmed and maximum heterogeneity and degradation were estimated. Purity of the SY124 used for spiking was determined using thermogravimetric analysis, high-performance liquid chromatography with UV detection, gas chromatography with flame ionisation and mass spectrometric detection, nuclear magnetic resonance and Karl Fischer titration. Full uncertainty budgets comprising all potential uncertainty sources were established. The following mass fractions were derived: ERM-EF317: 0.141 +- 0.018 mg.kg-1; ERM-EF318: 7.0 +- 0.04 mg.kg-1.JRC.D.2-Reference material

    Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms

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    On-scene drug detection is an increasingly significant challenge due to the fast-changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false-positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740–1070 nm small-wavelength-range near-infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true-positive and 98% true-negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on-scene approach is that the model can almost instantly adapt to changes in the illicit-drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited-range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on-site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food-safety, and environmental domains.</p

    Creating a reference database of cargo inspection X-ray images using high energy CT of cargo mock-ups

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    International audienceCustoms continue to use a wide range of technology in protecting against terrorism and the movement of illicit trade and prohibited imports. The throughput of scanned vehicles and cargo increases and just keeps on growing. Therefore, the need of automated algorithms to help screening officers in inspection, examination or surveillance of vehicles and containers is crucial. In this context, the successful collaboration between manufacturers and customs offices is of key importance. Facing this topic, within the seventh framework program of the European Commission, the project ACXIS “Automated Comparison of X-ray Images for cargo Scanning” arose. This project develops a reference database for X-ray images of illegal and legitimate cargo, procedures and algorithms to uniform X-ray images of different cargo scanners, and an automated identification of potentially illegal cargo
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