Abstract
Qualitative and quantitative chemometric models were evaluated to monitor moisture content of a wet granulation in a fluidised bed dryer using near infrared (NIR) technology. A principal component analysis (PCA) model was evaluated to obtain qualitative information. Multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLS-R) and support vector machine regression (SVM-R) were evaluated using The Unscrambler® X. The PLS-R method was selected to demonstrate real-time monitoring of the moisture content. An ABB FT-NIR spectrometer with a Galileo direct-contact fibre-optic diffuse reflectance probe was used for NIR measurements. The Unscrambler® X Process Pulse was employed to upload the PLS-R model in measuring real-time moisture content. The PCA model successfully projected test batch data for the process signature and the PLS-R model successfully predicted (root mean square error of calibration: 0.5799 and R2: 0.9898; root mean square error of cross validation: 0.6595 and R2: 0.9868) the moisture content of the granulation during fluidised bed drying. Future work includes implementing the developed models for routine manufacture of drug product at commercial scales. Real-time determination of the end point for loss on drying (LOD) will eliminate the conventional, off-line LOD measurements currently in practice.
© 2014 IM Publications LLP
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription