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.
Original languageEnglish
Number of pages1
Publication statusPublished - 30 Aug 2021
Event21st International Symposium on Industrial Crystallisation - Online
Duration: 30 Aug 20212 Sep 2021
https://dechema.de/en/ISIC_2021.html

Conference

Conference21st International Symposium on Industrial Crystallisation
Abbreviated titleISIC 21
Period30/08/212/09/21
Internet address

Keywords

  • process analytical technologies (PAT)
  • deep learning
  • in-situ imaging
  • particle size

Fingerprint

Dive into the research topics of 'Multidimensional particle characterisation from in-situ imaging using deep learning and transfer learning'. Together they form a unique fingerprint.

Cite this