A micro-XRT image analysis and machine learning methodology for the characterisation of multi- particulate capsule formulations [Video Abstract]

Doerr, Frederik J.S. and Florence, Alastair J. (2020) A micro-XRT image analysis and machine learning methodology for the characterisation of multi- particulate capsule formulations [Video Abstract]. Elsevier, Amsterdam.

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Doerr_Florence_IJPX_2020_image_analysis_and_machine_learning_methodology_for_the_characterisation_of_multi_particulate_capsule_video.mp4
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    Abstract

    Video abstract highlighting aspects of a micro-XRT image analysis and machine learning methodology for the characterisation of multi-particulate capsule formulations (https://doi.org/10.1016/j.ijpx.2020.100041).

    Creators(s): Doerr, Frederik J.S. ORCID logoORCID: https://orcid.org/0000-0001-5245-0503 and Florence, Alastair J. ORCID logoORCID: https://orcid.org/0000-0002-9706-8364;
    Item type:Other
    ID code:71463
    Notes:7MB; Online
    Keywords:pharmaceutical formulation development, watershed image sementation, crystallisation, XRT particle structure analysis, machine learning, micro-XRT, Therapeutics. Pharmacology, Pharmaceutical Science
    Subjects:Medicine > Therapeutics. Pharmacology
    Department:Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences
    Technology and Innovation Centre > Continuous Manufacturing and Crystallisation (CMAC)
    Strategic Research Themes > Advanced Manufacturing and Materials
    Depositing user: Pure Administrator
    Date deposited:14 Feb 2020 00:03
    Last modified:14 Jan 2021 03:14
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/71463
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