Many quality attributes of pharmaceutical suspensions are directly influenced by the physical properties of constituting particles. The influence has not been explored fully as the chemical characteristics (composition/concentration) are of primary concern. Particle size distribution provides with detailed characteristics of the material population, however, it is a challenging parameter to monitor during the process, due to limitations of the available analytical techniques.In this thesis, amethodology which analyses particle size distribution and concentration using the optical scattering properties extracted from a novel spatially and angularly resolved diffuse reflectance measurement (SAR-DRM) is proposed. This approach incorporates Mie theory and particle size distribution function, followed by Farrell's diffuse approximation to model specified reflectance signal. The developed methodology does not require a calibration model and was used to investigate three main objectives. Firstly, the sensitivity of the simulated SAR-DRM to the mode radius, distribution width and concentration of solids variations is explored.An inversion algorithm for the measured SAR-DRM is proposed and studied on polystyrene suspensions sample sets. Lastly, the methodology is then expanded to incorporate the particle shape parameter in the form of aspect ratio. Tomy knowledge, this study is a unique attempt to directly invert SAR-DRM to obtain the particle size distribution and concentration parameters from modelled and experimental measurements as well as the first study of particle shape effect on SAR-DRM.The simultaneous inversion of all three parameters of interest produced results comparable with reference measurements. The study provides a set of method limitations that when considered allow the optimal estimation of the parameters of interest. The results produced by the methodology developed and presented throughout this thesis suggest the great potential of using the methodology as a calibration-free tool for robust and reliable in-process analysis of pharmaceutical suspensions.
|Date of Award||8 Jul 2020|
- University Of Strathclyde
|Sponsors||University of Strathclyde|
|Supervisor||Yi-Chieh Chen (Supervisor) & Leo Lue (Supervisor)|