Publication: Modeling of drug release from matrix tablets with process variables of microwave-assisted modification of arrowroot starch using artificial neural network
Issued Date
2011-01-11
Resource Type
ISSN
10226680
Other identifier(s)
2-s2.0-78650964509
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Advanced Materials Research. Vol.152-153, (2011), 1700-1703
Suggested Citation
Suchada Piriyaprasarth, Pornsak Sriamornsak, Maneerat Juttulapa, Satit Puttipipatkhachorn Modeling of drug release from matrix tablets with process variables of microwave-assisted modification of arrowroot starch using artificial neural network. Advanced Materials Research. Vol.152-153, (2011), 1700-1703. doi:10.4028/www.scientific.net/AMR.152-153.1700 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/11907
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Modeling of drug release from matrix tablets with process variables of microwave-assisted modification of arrowroot starch using artificial neural network
Other Contributor(s)
Abstract
The objective of this study was to model the drug release property in terms of process variables of microwave-assisted modification of arrowroot starch using artificial neural network (ANN). The water content, microwave power and heating time were used as process variables for modification of arrowroot starch and the mean dissolution time was used as dependent variable. The correlation between process variables and dependent variable was examined using feed-forward back-propagation neural networks. The ANN model was optimized by considering goodness-of-fit and crossvalidated predictability. A "leave-one-out" cross-validation revealed that the neural network model could predict MDT values from matrix tablets with a reasonable accuracy (predictive r 2 of 0.824 and predictive root mean square error of 19.53). The predictive ability of these models was validated by a set of 4 formulations that were not included in the training set. The predicted and observed MDT were well correlated. © (2011) Trans Tech Publications, Switzerland.