Low-content quantification in powders using Raman spectroscopy: a facile chemometric approach to sub 0.1% limits of detection.

Abstract

A robust and accurate analytical methodology for low-content (<0.1%) quantification in the solid-state using Raman spectroscopy, sub-sampling, and chemometrics was demonstrated using a piracetam–proline model. The method involved a 5-step process: collection of relatively large number of spectra (8410) from each sample by Raman mapping, meticulous data pretreatment to remove spectral artefacts, use of a 0–100% concentration range partial least squares (PLS) regression model to estimate concentration at each pixel, use of a more-accurate, reduced concentration range PLS model to accurately calculate analyte concentration at each pixel, and finally statistical analysis of all 8000+ concentration predictions to produce an accurate overall sample concentration. The relative prediction accuracy was ~2.4% for a 0.05~1.0% concentration range and the limit of detection was comparable to high performance liquid chromatography (0.03% versus 0.041%). For data pretreatment, we developed a unique cosmic ray removal method and used an automated baseline correction method, neither of which required subjective user intervention and thus were fully automatable. The method is applicable to systems, which cannot be easily analyzed chromatographically such as hydrate, polymorph, or solvate contamination.