A system using energy dispersive X-ray diffraction has been tested to detect the presence of illicit drugs concealed within parcels typical of those which are imported into the UK via postal and courier services. The system was used to record diffraction data from calibration samples of diamorphine (heroin) and common cutting agents and a partial least squares regression model was established between diamorphine concentration and diffraction spectra. Parcels containing various crystalline and amorphous materials, including diamorphine, were then scanned to obtain multiple localised diffraction spectra and to form a hyperspectral image. The calibration model was used for the prediction of diamorphine concentration throughout the volume of parcels and enabled the presence and location of diamorphine to be determined from the visual inspection of concentration maps. This research demonstrates for the first time the potential of an EDXRD system to generate continuous hyperspectral images of real parcels from volume scanning in security applications and introduces the opportunity to explore hyperspectral image analysis in chemical and material identification. However, more work must be done to make the system ready for implementation in border control operations by bringing down the procedure time to operational requirements and by proving the system's portability.
Energy dispersive X‐ray diffraction (EDXRD) and maximum likelihood principal component analysis multivariate curve resolution‐alternating least squares (MLPCA‐MCR‐ALS) with correlation constraint were used to quantify the composition of packaged pharmaceutical formulations. Recorded EDXRD profiles from unpackaged and packaged samples of ternary mixtures were modelled together in order to recover the concentrations as well as the pure profiles of the constituent compounds. MLPCA was used as a data pretreatment step to MCR‐ALS, accounting for the high noise and nonconstant variance observed in the EDXRD profiles and was shown to improve the resolution accuracy of MCR‐ALS for the data set. Local correlation constraints were applied in the MCR‐ALS procedure in order to model unpackaged and packaged samples simultaneously while accounting for the matrix effect of the packaging materials. The composition of the formulations was estimated with root‐mean‐square error of prediction for each component, including paracetamol, being approximately 2.5 %w/w for unpackaged and packaged samples. Paracetamol concentration was resolved simultaneously for the unpackaged and packaged samples to a greater degree of accuracy than achieved by partial least squares regression (PLSR) when modelling the contexts separately. By modelling the effects of the packaging and incorporating accurate reference information of unpackaged samples into the resolution of packaged samples, the potential of EDXRD and MLPCA‐MCR‐ALS for the identification and quantification of packaged solid‐dosage medicine in nondestructive screening and counterfeit medicine detection has been raised.
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