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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., The novel application of 740‐1070 nm small wavelength range NIR spectroscopy to confidently detect cocaine in case samples is demonstrated. Multi‐stage machine learning algorithms are applied to exploit the limited spectral features and predict not only the presence of cocaine but also predict a concentration and sample composition. A model based on >10,000 spectra from case samples yielded 97% true positive and 98% true negative results. The practical applicability is shown on over 100 case samples not included in model design.

X-ray screening of containers and unit load devices in the area of cargo shipping is becoming an essential and common feature at ports and airports all over the world. The detection of prohibited items in X-ray images is a challenging task for screening officers as they need to know which items are prohibited and what they look like in X-ray images. The main aim of this study was to investigate whether X-ray image interpretation competency of cargo security screeners can be increased by computer-based training. More specifically, effects of training were investigated by conducting tests before training started and after approximately three months of training. Moreover, it was examined whether viewing X-ray images in pseudo color would lead to a better detection performance compared to when X-ray images are shown in greyscale. Recurrent computer-based training resulted in large performance increases after three months. No significant difference in detection performance could be found for tests when using X-ray images in greyscale vs. pseudo color. Relevance to industry Cargo X-ray screening is becoming a common feature at ports and airports. The identification and detection of prohibited items in X-ray images highly depends on human operators and their competences regarding X-ray image interpretation. Thus, research on appropriate training methods and enhancements of the human factor are essential to achieve and maintain high levels of security.

Conference of 50th Annual IEEE International Carnahan Conference on Security Technology, ICCST 2016 ; Conference Date: 24 October 2016 Through 27 October 2016; Conference Code:125934; International audience; Customs administrations are responsible for the enforcement of fiscal integrity and security of movements of goods across land and sea borders. In order to verify whether the transported goods match the transport declaration, X-ray imaging of containers is used at many customs site worldwide. The main objective of the research and development project 'Automated Comparison of X-ray Images for Cargo Scanning (ACXIS)', which is funded by the European 7th Framework Program, is to improve the efficiency and effectiveness of the inspection procedures of cargo at customs using X-ray technology. The current inspection procedures are reviewed to identify risks, catalogue illegal cargo, and prioritize detection scenarios. Based on these results, we propose an integrated solution that provides automation, information exchange between customs administrations, and computer-based training modules for customs officers. Automated target recognition (ATR) functions analyze the X-ray image after a scan is made to detect certain types of goods such as cigarettes, weapons and drugs in the freight or container. Other helpful information can also be provided, such as the load homogeneity, total or partial weight, or the number of similar items. The ATR functions are provided as an option to the user. The X-ray image is transformed into a manufacturer-independent format through geometrical and spectral corrections and stored into a database along with the user feedback and other related data. This information can be exchanged with similar systems at other sites, thus facilitating information exchange between customs administrations. The database is seeded with over 30'000 examples of legitimate and illegal goods. These examples are used by the ATR functions through machine learning techniques, which are further strengthened by the information exchange. In order to improve X-ray image interpretation competency of human operators (customs officers), a computer-based training software is developed that simulates these new inspection procedures. A study is carried out to validate the effectiveness and efficiency of the computer-based training as well as the implemented procedures. Officers from the Dutch and Swiss Customs administrations partake in the study, covering both land and sea borders.

International audience; Customs 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.

Two reference materials with certified mass fractions of SY124 in gas oil have been prepared. Samples were prepared by the spiking of blank gas oil with pure SY124. Homogeneity and stability were confirmed, and maximum heterogeneity and degradation were estimated. The purity of the SY124 used for spiking was determined using thermogravimetric analysis, high-performance liquid chromatography with UV detection, gas chromatography with flame ionization 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 ± 0.4 mg kg-1.

The European Union's reference method for the determination of a common fiscal marker of gas oil for heating purposes, Solvent Yellow 124 (SY124), was validated. A total of 12 different batches of samples using various commercially available gas oils and various colorants with SY124 concentrations from 0.12 to 9 mg L-1 were prepared. Various other dyes were added to check for potential interferences in the separation and detection of SY124. A total of 26 laboratories participated in the validation study. Outliers were identified using the Cochran and Hawkins test, and the resulting datasets were checked for normal distribution using normal probability plots. At a level of 6 mg L-1, the relative repeatability and reproducibility standard deviations were 0.68% and 3.8%, respectively. At 0.12 mg L-1, the repeatability and reproducibility standard deviations were 5.4% and 13.5%. A limit of detection of 0.020 mg L-1 and a limit of quantification of 0.065 mg L-1 were estimated. The method was found to be withou...