Process analytical technology: chemometric analysis of Raman and near infra-red spectroscopic data for predicting...

The purpose of this work was to develop a correlation between pharmaceutical properties such as hardness, porosity, and content with prediction models employed using Raman and near infra-red (NIR) spectroscopic methods. Metoprolol tartrate tablets were prepared by direct compression and wet granulation methods. NIR spectroscopy and chemical imaging, and Raman spectra were collected, and hardness, porosity, and dissolution were measured. The NIR PLS model showed a validated correlation coefficient of >0.90 for the predicted versus measured porosity, hardness, and amount of drug with raw and second derivative NIR spectra. Raman spectra correlated porosity of the tablets using raw data for directly compressed tablets and wet granulated tablets (r(2) > 0.90). A very close root-mean square error of calibration (RMSEC) and root-mean square error of prediction (RMSEP) values were found in all the cases indicating validity of the calibration models. Raman spectroscopy was used for the first time to predict physical quality attribute such as porosity successfully. Chemical imaging utilizing NIR detector also demonstrated to show physical changes due to compression differences. In conclusion, sensor technologies can be potentially used to predict physical parameters of the matrix tablets.