Multidimensional particle characterisation from in-situ imaging using deep learning and transfer learning

Boyle, Christopher and Ferreira, Carla and Chen, Yi-Chieh and Tachtatzis, Christos and Andonovic, Ivan and Brown, Cameron and Sefcik, Jan and Cardona, Javier (2021) Multidimensional particle characterisation from in-situ imaging using deep learning and transfer learning. In: 21st International Symposium on Industrial Crystallisation, 2021-08-30 - 2021-09-02, Online.

[thumbnail of Boyle-etal-ISIC-2021-Multidimensional-particle-characterisation-from-in-situ-imaging-using-deep-learning]
Preview
Text. Filename: Boyle_etal_ISIC_2021_Multidimensional_particle_characterisation_from_in_situ_imaging_using_deep_learning.pdf
Final Published Version

Download (3MB)| Preview

Abstract

Particle size and shape are important in the pharmaceutical industry, affecting both process efficiency and product performance. Quality-by-design and continuous manufacturing are aided with appropriate models of processes — selection and calibration of which are informed by measurement of particle size and shape. Off-line measurements have inherent limitations when following the trajectory of particle attributes in a process; removing and treating material for off-line analysis can alter particle characteristics. In contrast, in-line measurements provide representative measures of particle size and shape at the expense of producing more challenging (out of focus, overlapping particles) datasets for extraction of particle characteristics.