A novel preformulation tool to group microcrystalline celluloses using artificial neural network and data clustering.
OBJECTIVE
To group microcrystalline celluloses (MCCs) using a combination of artificial neural network (ANN) and data clustering.


METHODS
Radial basis function (RBF) network was used to model the torque measurements of the various MCCs. Output from the RBF network was used to group the MCCs using a data clustering technique known as discrete incremental clustering (DIC). Rheological or torque profiles of various MCCs at different combinations of mixing time and water:MCC ratios were obtained using mixer torque rheometry (MTR). Correlation analysis was performed on the derived torque parameter Torque(max) and physical properties of the MCCs.


RESULTS
Depending on the leniency of the predefined threshold parameters, the 11 MCCs can be assigned into 2 or 3 groups. Grouping results were also able to identify bulk and tapped densities as major factors governing water-MCC interaction. MCCs differed in their water retentive capacities whereby the denser Avicel PH 301 and PH 302 were more sensitive to the added water.


CONCLUSIONS
An objective grouping of MCCs can be achieved with a combination of ANN and DIC. This aids in the preliminary assessment of new or unknown MCCs. Key properties that control the performance of MCCs in their interactions with water can be discovered.
Soh
Josephine L P
JL
National University of Singapore, Department of Pharmacy, Singapore 117543.
Chen
Fei
F
Liew
Celine V
CV
Shi
Daming
D
Heng
Paul W S
PW
D002482:Cellulose / Q000032:analysis* / Q000737:chemistry*; D002626:Chemistry, Pharmaceutical; D016000:Cluster Analysis; D016571:Neural Networks, Computer
D002482:Cellulose; C109691:microcrystalline cellulose