Application of Near-Infrared Spectroscopy to statistical control in freeze-drying processes
| Autor: | Serena Bobba, Davide Fissore, Nunzio Zinfollino |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Quality Control
Sucrose Control chart Chemistry Pharmaceutical Pharmaceutical Science Control theory Partial least squares regression Statistics Spectroscopy Fourier Transform Infrared Near-Infrared Least-Squares Analysis Spectroscopy Mathematics Near-Infrared Spectroscopy Freeze-drying Statistical control Multivariate analysis Discriminant analysis Control chart Mahalanobis distance Statistical control Final product Discriminant Analysis Water General Medicine Linear discriminant analysis Statistical process control Freeze Drying Pharmaceutical Preparations Freeze-drying Multivariate Analysis Hotelling's T-squared distribution Biotechnology |
| Zdroj: | European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V. 168 |
| ISSN: | 1873-3441 |
| Popis: | Batch freeze-drying of pharmaceutical products in vials may result in a high degree of intra-batch variability due to several reasons, e.g. non uniform heating rate in the drying chamber. Therefore, product quality in the final product has to be checked in a statistically significant number of samples, in particular in the stage of process development. Here, Fourier-Transform Near-Infrared Spectroscopy is proposed as a fast, non-destructive technique for an off-line Statistical Quality Control application. At first, results obtained in a batch where product features are satisfactory are used to identify a target quality threshold. Then, a statistical controller is developed in such a way that in a production run it is possible to quickly check if product quality exceeds the desired threshold or not. Two approaches based on multivariate analysis are presented: one employs the Hotelling T2 and Mahalanobis statistics to calculate control charts, the other is an application of Partial Least Squares for discriminant analysis (PLS-DA). Control charts and PLS-DA were trained with samples obtained in a run where sucrose solution was processed and validated in other runs where the final product was known to have the desired qualitative characteristics or not. Overall, out-of-specification samples can be predicted by control charts and PLS-DA with 99% and 98% accuracy respectively. PLS-DA was shown to be able to better identify samples correctly processed, while the control charts where more accurate to identify vials where something went wrong. Focusing on residual moisture of the final product, all samples where it was higher than the target value were always correctly identified. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect