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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. 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. A multiset data structure was used to model and resolve concentrations and pure signals of unpackaged and packaged samples simultaneously. MLPCA enabled greater accuracy of resolution. The potential application of EDXRD and MLPCA‐MCR‐ALS in counterfeit medicine screening is demonstrated. [ABSTRACT FROM AUTHOR]

Preliminary studies have shown the effectiveness of multivariate analysis (MVA) for drug identification from energy-dispersive X-ray diffraction patterns. A statistical model to predict drug content from the diffraction profile of a sample of mixed composition was developed by applying MVA to both experimental and simulated data. Separate data-sets were used for building and testing the models. Both experimental and simulated data were used and the MVA predictions compared. Experimental data included diffraction patterns from small (5 mm diameter) drug samples with various cutting agents, acquired with a HPGe detector; simulated data included diffraction patterns of samples including materials simulating drugs (i.e., materials featuring sharp diffraction peaks in the relevant momentum transfer range) and typical packaging materials. Both a HPGe detector (energy resolution 0.7 keV at 59.5 keV) and a CZT detector (energy resolution 4 keV at all energies) were simulated. MVA was used to predict the drug content. In all cases different statistics were applied to assess the detection limits of the models. Multivariate analysis has proved effective in both identifying the presence of a drug and its concentration. Due to the large contribution to peak broadening given by angular resolution, no significant decrease in accuracy has been found when using CZT with respect to HPGe data. [ABSTRACT FROM AUTHOR]

Abstract: Energy dispersive X-ray diffraction is proposed as a suitable non-destructive method to rapidly identify illicit drugs in parcels. A preliminary data set of 7 illicit drug samples and a possible cutting agent has been collected with a range of count times using a tungsten target X-ray source, a high resolution HpGe detector and a variable geometry diffraction cell. These results have been used to calibrate and train multivariate analysis software to predict the drug content in previously unseen spectra. [Copyright &y& Elsevier]