Characterization of chloramphenicol palmitate drug polymorphs by Raman mapping with multivariate image segmentation...

Area of Science:

• Chemical imaging
• Materials science
• Pharmaceutical analysis

Background:

• Chemical imaging provides high-resolution molecular insights into sample heterogeneity.
• Understanding sample composition is crucial for pharmaceutical quality control.

Purpose of the Study:

• To implement Raman mapping with a novel multivariate image segmentation approach for characterizing chloramphenicol palmitate polymorphs in tablets.
• To develop a robust method for identifying and visualizing the spatial distribution of different polymorphs.

Main Methods:

• Utilized Raman mapping for microscopic chemical analysis.
• Developed and applied a new multivariate image segmentation approach based on spatial directed agglomeration clustering.
• Incorporated spatial closeness and spectral similarity for pixel clustering and image segmentation.

Main Results:

• Successfully identified distinct chloramphenicol palmitate polymorphs within tablets.
• Achieved quantitative visualization of the spatial distribution of identified polymorphs.
• Demonstrated improved segmentation robustness by integrating spatial proximity into clustering.

Conclusions:

• The proposed Raman mapping and image segmentation technique enables accurate, non-invasive polymorph identification and distribution analysis.
• This method shows significant potential for rapid, quantitative polymorph analysis in pharmaceutical manufacturing.
• The spatial directed agglomeration clustering approach enhances image segmentation accuracy and automation.