Please use this identifier to cite or link to this item:
|Title:||A novel preformulation tool to group microcrystalline celluloses using artificial neural network and data clustering||Authors:||Soh, J.L.P.
|Keywords:||Artificial neural network
Discrete incremental clustering
Mixer torque rheometry
|Issue Date:||Dec-2004||Citation:||Soh, J.L.P., Chen, F., Liew, C.V., Shi, D., Heng, P.W.S. (2004-12). A novel preformulation tool to group microcrystalline celluloses using artificial neural network and data clustering. Pharmaceutical Research 21 (12) : 2360-2368. ScholarBank@NUS Repository. https://doi.org/10.1007/s11095-004-7690-6||Abstract:||Purpose. 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 Torquemax 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. © 2004 Springer Science+Business Media, Inc.||Source Title:||Pharmaceutical Research||URI:||http://scholarbank.nus.edu.sg/handle/10635/105577||ISSN:||07248741||DOI:||10.1007/s11095-004-7690-6|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 16, 2020
WEB OF SCIENCETM
checked on Sep 16, 2020
checked on Sep 19, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.