Visualization of particle size and shape distributions using self-organizing maps

In pharmaceutical process technology, characterization of the sizes and shapes of different particles is essential. However, comparisons and analysis of different size and shape characteristics of particles are very difficult. In this investigation, we used the self-organizing map (SOM) to visualize the size and shape distributions obtained with image analysis (IA) of a series of model particles and particles created by fluidized bed granulation. Thereafter, the SOM visualization was compared to principal component analysis (PCA) results of the same data. This study shows that the self-organizing map is a useful and interpretive method for analysis of large data sets of particle size and shape distributions. The results indicate that the self-organizing map was capable of creating an intuitive presentation of the differences in the studied particle populations. The choice of data analysis tools should always be made with great consideration.