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Title: Non-destructive detection of counterfeit and substandard medicines using X-ray diffraction
Author: Crews, C. C. E.
ISNI:       0000 0004 7231 8376
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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The prevalence of counterfeit and substandard medicines has been growing rapidly over the past decade, and fast, non-destructive techniques for their detection are urgently needed to counter this trend. In this thesis, both energy-dispersive X-ray diffraction (EDXRD) and pixelated diffraction (“PixD”) combined with chemometric methods were assessed for their effectiveness in detecting poor-quality medicines within their packaging. Firstly, a series of caffeine, paracetamol and cellulose mixtures of known concentrations were pressed into tablets. EDXRD spectra of each tablet were collected both with and without packaging. Principal component analysis (PCA) and partial least-squares regression (PLSR) were used to study the data and construct calibration models for quantitative analysis. The concentration prediction errors for the packaged data were found to be very similar to those obtained in the unpackaged case, and were also on a par with reported values in the literature using higher-resolution angular-dispersive X-ray diffraction (ADXRD). Following this, soft independent modelling by class analogy (SIMCA) classification was used to compare EDXRD spectra from a test set of over-the-counter (OTC) medicines containing various combinations of active pharmaceutical ingredients (APIs) against PCA models constructed using spectra collected for paracetamol and ibuprofen samples. The test samples were selected to emulate different levels of difficulty in authenticating medicines correctly, ranging from completely different APIs (easy) to those with a small quantity of additional API (difficult). This classification study found that the sensitivity and specificity were optimal at data acquisition times on the order of 75~150s, and regardless of whether layers of blister and card packaging surrounded the tablet in question. This experiment was repeated on a novel, compact system incorporating a pixellated detector, which was found to reduce the required data acquisition times for optimal classification by a factor of five.
Supervisor: Speller, R. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available