Publication

Quantitative Image Analysis for Evaluating the Coating Thickness and Pore Distribution in Coated Small Particles

Laksmana, F. L., Van Vliet, L. J., Hartman Kok, P. J. A., Vromans, H., Frijlink, H. W. & Van der Voort Maarschalk, K., Apr-2009, In : Pharmaceutical Research. 26, 4, p. 965-976 12 p.

Research output: Contribution to journalArticleAcademicpeer-review

This study aims to develop a characterization method for coating structure based on image analysis, which is particularly promising for the rational design of coated particles in the pharmaceutical industry.

The method applies the MATLAB image processing toolbox to images of coated particles taken with Confocal Laser Scanning Microscopy (CSLM). The coating thicknesses have been determined along the particle perimeter, from which a statistical analysis could be performed to obtain relevant thickness properties, e.g. the minimum coating thickness and the span of the thickness distribution. The characterization of the pore structure involved a proper segmentation of pores from the coating and a granulometry operation.

The presented method facilitates the quantification of porosity, thickness and pore size distribution of a coating. These parameters are considered the important coating properties, which are critical to coating functionality. Additionally, the effect of the coating process variations on coating quality can straight-forwardly be assessed.

Enabling a good characterization of the coating qualities, the presented method can be used as a fast and effective tool to predict coating functionality. This approach also enables the influence of different process conditions on coating properties to be effectively monitored, which latterly leads to process tailoring.

Original languageEnglish
Pages (from-to)965-976
Number of pages12
JournalPharmaceutical Research
Volume26
Issue number4
Publication statusPublished - Apr-2009

    Keywords

  • Algorithms, Cellulose, Dosage Forms, Image Processing, Computer-Assisted, Methylcellulose, Microscopy, Confocal, Models, Statistical, Particle Size, Porosity, Technology, Pharmaceutical, Time Factors

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