Investigating microcrystalline cellulose crystallinity using Raman spectroscopy

Area of Science:

• Pharmaceutical Sciences
• Materials Science
• Analytical Chemistry

Background:

• Microcrystalline cellulose (MCC) exhibits variable crystallinity influenced by raw material and manufacturing.
• This variability can lead to inconsistencies in downstream pharmaceutical processes.
• Accurate and rapid assessment of MCC crystallinity is essential for quality control.

Purpose of the Study:

• To develop and validate models for determining the crystallinity index (%CI) of MCC using Raman spectroscopy.
• To compare the effectiveness of different Raman probe sizes (100 µm and 6 mm) for crystallinity assessment.
• To establish a rapid method for MCC crystallinity analysis, reducing processing time.

Main Methods:

• Raman spectra were acquired from 30 commercial MCC batches using 100 µm (MR probe) and 6 mm (PhAT probe) spot sizes.
• Principal Component Analysis (PCA) was used to differentiate spectra obtained from different probes.
• Partial Least Squares (PLS) regression models were developed to predict %CI from Raman spectra, alongside a univariate model adjusted for each probe.

Main Results:

• PCA effectively separated spectra acquired with the MR and PhAT probes.
• Both univariate and PLS models demonstrated adequate predictive power for MCC %CI.
• The PLS model significantly reduced analysis time by eliminating the need for spectral deconvolution.
• A general reference amorphous spectrum was proposed for each instrument to improve model accuracy.

Conclusions:

• Raman spectroscopy, particularly with PLS modeling, provides a robust and efficient method for quantifying MCC crystallinity.
• The developed models and web application facilitate rapid, on-site assessment of MCC quality.
• This approach helps mitigate downstream process variability associated with MCC crystallinity differences.