Evaluation of Transmission Raman spectroscopy and NIR Hyperspectral Imaging for the assessment of content uniformity in solid oral dosage forms✰
| Autor: | Sven-Oliver Arndt, Valentina Manici, Manuela Shaukat, Stefan Busche, Carsten Schmidt, Netsanet Fekadu Belay |
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| Rok vydání: | 2021 |
| Předmět: |
Spectroscopy
Near-Infrared Materials science Process analytical technology Analytical chemistry Pharmaceutical Science Hyperspectral imaging Hyperspectral Imaging Derivative Spectrum Analysis Raman Dosage form Transmission Raman spectroscopy Chemometrics Calibration Content (measure theory) Least-Squares Analysis Tablets |
| Zdroj: | European Journal of Pharmaceutical Sciences. 166:105963 |
| ISSN: | 0928-0987 |
| DOI: | 10.1016/j.ejps.2021.105963 |
| Popis: | Purpose : The objective of the present study was to explore and compare fast and non-destructive Transmission Raman Spectroscopy (TRS) and Near Infrared Hyperspectral imaging (NIR HSI) for the development of predictive quantitative methods to determine content uniformity (CU) of tablets. Methods : A set of single Active Pharmaceutical Ingredients (API) tablets with nine concentration levels of caffeine ranging from 12.75%w/w to 17.75%w/w and another set of double API tablets with five concentration levels of model API A* (5.25%w/w - 9.25%w/w) and caffeine (7%w/w – 13%w/w) were prepared. Chemometric prediction models were developed using partial least square (PLS 1) and later tested using a test set for both single and double API tablets. Results Calibration PLS1 models were developed for both single and double APIs using a combination of S-G 1st derivative and SNV data pre-processing steps that offer an optimal model performance with the lowest cross-validation error and bias. The root mean square error of prediction (RMSEP) for the PLS1 model for single API caffeine tablets using TRS and NIR HSI was 0.27% and 0.36% respectively. The RMSEP for the PLS1 models built using TRS for the double API tablets was 0.29% for API A and 0.34% for caffeine. Similarly, for the NIR HIS prediction models the RMSEP was 0.43% for API A and 0.56% for caffeine. Conclusion Overall TRS presented a 25–30% more accurate prediction capability compared to NIR HSI in this specific sample sets. Nevertheless, both TRS ad NIR HSI possess the potential to be employed as rapid, nondestructive techniques to replace classical wet- chemistry methods for at- or off-line determination of tablet CU. |
| Databáze: | OpenAIRE |
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