Use of the Kohonen self-organising map to predict the flowability of powders

A true-dimensional Kohonen self-organising map was used to predict weight variation in tablets on the basis of flow properties of powders. Flow test data of the powders consisted of four different angles of repose, flowing time and the non-uniformity of flow determined front thirty-six powder batches. The network was trained with three microcrystalline cellulose grades and their mixture. Microcrystalline cellulose fillers contained various amounts of a poor flowing drug substance, paracetamol. The final network was tested using a few silicified microcrystalline cellulose powders which also contained paracetamol. The Kohonen self-organising map is able to combine numerous parameters that describe powder flowability, and to present the data in an easily-understood, visual form. It is practically impossible for any other technique to achieve this. The self-organising map appeared to he able to cluster flowability data so that the best flowing powders were localised in the same part on the map. The predictive ability of the map was satisfactory.