Spatially and angularly resolved diffuse reflectance measurement for in-line analysis of particle suspensions, a multi-sensor approach

Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2019
Thesis identifier
  • T15544
Person Identifier (Local)
  • 201557363
Qualification Level
Qualification Name
Department, School or Faculty
  • Particle size and shape are critical quality attributes for active pharmaceutical ingredients (API) as they have direct impact on downstream processing, as well as on the performance behaviour of the finished product. None of the current process analytical technologies (PAT), although capable of providing an indication on these key attributes, measures particle size directly. Obtaining a reliable and robust quantitative information of these particle attributes in real-time remains a great challenge across the multiple manufacturing steps and cannot be achieved by just using a single sensor. A Spatially and Angularly-Resolved Diffuse Reflectance Measurement (SAR-DRM) technology is for the first time applied to monitor micron size particles. SAR-DRM relies on multiple scattering of particles and collects multi-wavelength (UV-visibleNIR) diffuse reflectance spectra from optical fibres of multi-angle multi-space arrangements. Each SAR-DRM spectrum yields differences which correspond to the light travelling differently in the sample and to being differently affected by scattering and absorption effects. This technique is used alongside with Focus Beam Reflectance Measurement (FBRM) and Particle Vision and Measurement (PVM) which have been widely applied for in-line monitoring of particle attributes in crystallisation processes, in order to examine the SAR-DRM performance and investigate the possibility to broadening the size and concentration ranges of the applications.;The investigation was carried out on two model systems: polystyrene beads suspensions in water and alpha lactose monohydrate suspensions in acetone, for size (<38 to 800 µm) and concentration (0.5 to 25 wt.%) ranges, relevant to many pharmaceutical processes, e.g., during crystallisation and granulation. As particles scatter light differently depending on the size, shape and solid concentration, the spectral changes in SAR-DRM can be related to the sample's attributes. These properties can be either obtained by applying light propagation theory, which involves intensive computational calculations and are challenging to invert the information in real-time, or by multivariate regression analysis, an alternative and faster method. Characterisation of the particles attributes was performed by both in-line and off-line commercial technologies, and served as an input to validate SAR-DRM sensitivity, accuracy and capability to track the differences in size and solid loading in the model system. Robust calibration models were established to predict particle size and particle concentration for the individual technologies and for the combined measurement data, by applying multivariate regression analysis.;The results suggest that the accuracy of multivariate calibration models can be improved by combining key SAR-DRM configurations with FBRM data. This is due to the spectra captured by SAR-DRM containing more complete information about the light scattering properties of samples, which can be related to the particle size and concentration. As SAR-DRM prefers high turbidity samples, i.e., samples with higher solid content, the study shows that SARDRM technology can be a potential complementary to other PAT tools such as FBRM and PVM, which tend to perform better for low turbidity samples. Currently, solid concentration is not measured during pharmaceutical processes and particle size is not directly obtained. Our result suggests that combining the strengths of each technique can help to obtain reliable and quantitative information about particle attributes, allowing to achieve robust process monitoring and enable improved control and optimisation of manufacturing processes.
Advisor / supervisor
  • Chen, Yi-Chieh
  • Sefcik, Jan
Resource Type
Date Created
  • 2019
Former identifier
  • 9912893392602996