Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level
| Autor: | Andrea Sekulovic, Wim Jiskoot, Jukka Rantanen, Adam D. Grabarek, Thomas Rades, R. Verrijk, Andrea Hawe |
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| Rok vydání: | 2021 |
| Předmět: |
Particle number
Clinical Biochemistry Analytical chemistry Pharmaceutical Science Raw material Spectrum Analysis Raman 01 natural sciences Analytical Chemistry symbols.namesake Impurity Drug Discovery Partial least squares regression Least-Squares Analysis Spectroscopy 010405 organic chemistry Chemistry 010401 analytical chemistry Discriminant Analysis Linear discriminant analysis 0104 chemical sciences Carbamazepine symbols Particle Prism Powders Raman spectroscopy |
| Zdroj: | Journal of Pharmaceutical and Biomedical Analysis Journal of Pharmaceutical and Biomedical Analysis, 193, 113744 |
| ISSN: | 0731-7085 |
| Popis: | Solid form diversity of raw materials can be critical for the performance of the final drug product. In this study, Raman spectroscopy, image analysis and combined Raman and image analysis were utilized to characterize the solid form composition of a particulate raw material. Raman spectroscopy provides chemical information and is complementary to the physical information provided by image analysis. To demonstrate this approach, binary mixtures of two solid forms of carbamazepine with a distinct shape, an anhydrate (prism shaped) and a dihydrate (needle shaped), were characterized at an individual particle level. Partial least squares discriminant analysis classification models were developed and tested with known, gravimetrically mixed test samples, followed by analysis of unknown, commercially supplied carbamazepine raw material samples. Classification of several thousands of particles was performed, and it was observed that with the known binary mixtures, the minimum number of particles needed for the combined Raman spectroscopy - image analysis classification model was approximately 100 particles per solid form. The carbamazepine anhydrate and dihydrate particles were detected and classified with a classification error of 1 % using the combined model. Further, this approach allowed the identification of raw material solid form impurity in unknown raw material samples. Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level has its potential in accurate detection of low amounts of unwanted solid forms in particulate raw material samples. |
| Databáze: | OpenAIRE |
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