Characterization of particle sizes in bulk pharmaceutical solids using digital image information

Area of Science:

• Materials Science
• Chemical Engineering
• Image Analysis

Background:

• Accurate particle size analysis is crucial for material properties.
• Traditional methods often require sample dispersion, which can be time-consuming or alter particle characteristics.
• Developing non-dispersive methods for powder characterization is an ongoing challenge.

Purpose of the Study:

• To present a novel method for extracting particle size information from undispersed bulk powder surfaces.
• To validate the use of digital surface imaging combined with multivariate modeling for particle size analysis.
• To demonstrate a fast and efficient particle size measurement technique.

Main Methods:

• Acquisition of digital surface images of granules under controlled illumination using a specialized optical setup.
• Generation and extraction of the gray scale difference matrix (GSDM) descriptor from surface images to characterize particle size.
• Development of a Partial Least Squares (PLS) model correlating GSDM data with particle size distribution measured by sieving.

Main Results:

• A strong correlation was achieved between the GSDM descriptor and granule size distribution (R² = 0.91, Q² = 0.87).
• The developed model accurately predicted particle sizes in the range of 20 to 2500 micrometers.
• The GSDM descriptor effectively captured textural information from powder surfaces, enabling particle size determination.

Conclusions:

• Digital images of undispersed powder surfaces contain significant information for particle size distribution analysis.
• The GSDM descriptor allows for the utilization of bulk powder surface information.
• This method offers a rapid and effective approach for particle size measurement without sample dispersion.