Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision
Abstract
This work presents a system, where deep learning was used on images captured with a digital camera to simultaneously determine the API concentration and the particle size distribution (PSD) of two components of a powder blend. The blend consisted of acetylsalicylic acid (ASA) and calcium hydrogen phosphate (CHP), and the predicted API concentration was found corresponding with the HPLC measurements. The PSDs determined with the method corresponded with those measured with laser diffraction particle size analysis.
This novel method provides fast and simple measurements and could be suitable for detecting segregation in the powder. By examining the powders discharged from a batch blender, the API concentrations at the top and bottom of the container could be measured, yielding information about the adequacy of the blending and improving the quality control of the manufacturing process.
Materials
Acetylsalicylic acid (ASA) was obtained from Molar Chemicals (Halásztelek, Hungary). Anhydrous calcium hydrogen phosphate (CHP) was acquired from JRS Pharma (Rosenberg, Germany). Fig 1 presents the microscopic images of the used materials.
Download the full study as PDF here: Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision
or read it here
Máté Ficzere, Orsolya Péterfi, Attila Farkas, Zsombor Kristóf Nagy, Dorián László Galata, Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision, European Journal of Pharmaceutical Sciences, Volume 191, 2023, 106611, ISSN 0928-0987,
https://doi.org/10.1016/j.ejps.2023.106611.
See the webinar:
“Rational Selection of Cyclodextrins for the Solubilization of Poorly Soluble Oral Drugs”, 8. November 2023:
Get more information & register here for free:
