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.
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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.
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Item type: Conference or Workshop Item(Poster) ID code: 77809 Dates: DateEvent30 August 2021Published10 November 2020AcceptedSubjects: Technology > Chemical engineering Department: Faculty of Engineering > Chemical and Process Engineering
Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences > Continuous Manufacturing and Crystallisation
Strategic Research Themes > Measurement Science and Enabling Technologies
Faculty of Engineering > Electronic and Electrical Engineering
Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences
Technology and Innovation Centre > Continuous Manufacturing and Crystallisation (CMAC)
Technology and Innovation Centre > BionanotechnologyDepositing user: Pure Administrator Date deposited: 16 Sep 2021 14:55 Last modified: 09 Apr 2024 06:53 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/77809