Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision.

Journal: European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
PMID: 37844806

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.

Authors

  • Máté Ficzere
    Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary.
  • Orsolya Péterfi
    Department of Drugs Industry and Pharmaceutical Management, University of Medicine, Pharmacy, Sciences and Technology of Târgu Mureș, Gheorghe Marinescu 38, 540139 Târgu Mureș, Romania.
  • Attila Farkas
    Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary.
  • Zsombor Kristóf Nagy
    Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
  • Dorián László Galata
    Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary. Electronic address: galata.dorian.laszlo@vbk.bme.hu.

Keywords

External Resources

Popular Topics
Recent Journals