Abstract
AbstractEnergy 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.
Highlights
Nondestructive volume characterisation of materials located beneath solid surface layers, such as the contents of packaged or concealed goods, requires high-energy photon flux to overcome attenuation effects
This study demonstrates that Energy dispersive X-ray diffraction (EDXRD) can be used with Multivariate curve resolution-alternating least squares (MCR-ALS) as a methodology to accurately determine the concentrations of constituent compounds within packaged pharmaceutical formulations of simple mixtures
Using maximum likelihood principal component analysis (MLPCA) as a pretreatment step and a local correlation constraint during the alternating least squares procedure, paracetamol concentrations were resolved for both subsets simultaneously to a greater degree of accuracy than partial least squares regression (PLSR) achieves, even when PLSR models the subsets separately
Summary
Nondestructive volume characterisation of materials located beneath solid surface layers, such as the contents of packaged or concealed goods, requires high-energy photon flux to overcome attenuation effects. Energy dispersive X-ray diffraction (EDXRD) can meet this requirement and measures the coherent scattering fields, which characterise the samples comprising crystalline and polycrystalline materials. The profiles have high spectral selectivity and can be parametrically fitted and crossreferenced against databases in order to characterise and identify the samples.[6] When performing in situ EDXRD for screening purposes, we must accept lower resolution profiles with broad peaks and high levels of noise in order to achieve surface penetration, rapid screening and portability.[6,7] The resulting profiles often have highly overlapping characteristic peaks making interpretation more difficult
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