A statistical approach to evaluate the potential use of compression parameters for classification of pharmaceutical powder materials

The current work aims to investigate whether a multivariate statistical approach could reveal latent structures in compression data and group powders with respect to their compression behavior in a way that is consistent with an earlier proposed classification system. Seventeen pharmaceutically relevant materials, exhibiting a wide range of mechanical properties, were used as supplied, compressed, and parameters from three commonly used powder compression models (Kawakita parameters a and b−1, the rearrangement index ab, the Shapiro f parameter and Heckel Py) were retrieved. Multivariate analysis of the compression parameters was done with a Principal Component Analysis (PCA). It was found that the latent structures could be divided into three main parts; the most variation was found in the direction associated with particle rearrangement, second largest variation was found in the direction described by the particle fragmentation propensity, and the least variation was found in the direction associated with the plasticity of the particles. This work demonstrates that a combination of the selected compression parameters could be utilized to find relevant differences in compression behavior for a wide range of materials, and that this information can be presented in an efficient way by applying multivariate data analysis techniques.