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

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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.

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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.

ORCID iDs

Boyle, Christopher ORCID logoORCID: https://orcid.org/0000-0001-8926-6590, Ferreira, Carla ORCID logoORCID: https://orcid.org/0000-0002-0592-8540, Chen, Yi-Chieh ORCID logoORCID: https://orcid.org/0000-0002-8307-0666, Tachtatzis, Christos ORCID logoORCID: https://orcid.org/0000-0001-9150-6805, Andonovic, Ivan ORCID logoORCID: https://orcid.org/0000-0001-9093-5245, Brown, Cameron ORCID logoORCID: https://orcid.org/0000-0001-7091-1721, Sefcik, Jan ORCID logoORCID: https://orcid.org/0000-0002-7181-5122 and Cardona, Javier ORCID logoORCID: https://orcid.org/0000-0002-9284-1899;
CORE (COnnecting REpositories)