Quantification of pharmaceuticals via transmission Raman spectroscopy: data sub-selection

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Chemometrics

Background:

• Quantitative analysis of pharmaceutical formulations is crucial.
• Traditional mid-wavenumber Raman spectroscopy (340-2000 cm(-1)) has limitations.
• Data sub-selection methods can improve analytical models.

Purpose of the Study:

• To systematically characterize data sub-selection using multivariate analysis for Transformed Raman Spectroscopy (TRS).
• To compare sparse partial least squares (SPLS) with principal component analysis (PCA) for TRS data.
• To evaluate the utility of low-wavenumber Raman data (50-340 cm(-1)) for quantitative modeling.

Main Methods:

• Application of sparse partial least squares (SPLS) to Transformed Raman Spectroscopy (TRS) data for the first time.
• Comparison of SPLS with principal component analysis (PCA) for data sub-selection.
• Investigation of a model pharmaceutical formulation with two polymorphs (1-99% mixture).
• Analysis of both low-wavenumber (50-340 cm(-1)) and mid-wavenumber (340-2000 cm(-1)) spectral regions.

Main Results:

• Low-wavenumber Raman data (50-340 cm(-1)) demonstrated superior performance for quantitative modeling compared to mid-wavenumber data.
• Sparse partial least squares (SPLS) proved effective for data sub-selection in TRS.
• The study successfully characterized data sub-selection strategies for quantitative analysis.

Conclusions:

• Low-wavenumber Raman spectroscopy, combined with data sub-selection, significantly enhances quantitative analytical capabilities.
• This approach has potential applications in pharmaceuticals, security, and process-analytical technology.
• The findings pave the way for improved analytical methods using TRS and specialized optics.