Abstract
In the pharmaceutical industry, dissolution testing is part of the target product quality that is essential in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. Raman and near-infrared (NIR) spectroscopies are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods by creating partial least squares models to predict the content of the pills. The predicted values are then used along with the measured compression force as input data to Random Decision Forests in order to predict the dissolution profiles of the scanned tablets. It was found that Random Decision Forests models were able to predict the dissolution profile within the acceptance limit of the f2 factor.
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Acknowledgments
Project no. FIEK_16-1-2016-0007 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Centre for Higher Education and Industrial Cooperation Research infrastructure development (FIEK_16) funding scheme.
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Mrad, M.A., Csorba, K., Galata, D.L., Nagy, Z.K., Nagy, B. (2023). Spectroscopy-Based Prediction of In Vitro Dissolution Profile Using Random Decision Forests. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_35
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