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

Abstract

We report the first systematic characterisation of data sub-selection with multivariate analysis to be applied to either TRS or the low-wavenumber Raman region. A model pharmaceutical formulation comprising two polymorphs mixed in the range of 1–99% is investigated. For data sub-selection, sparse partial least squares is for the first time applied to TRS data and compared with principal component analysis. It is found that low-wavenumber data (50–340 cm⁻¹) are demonstrably superior for quantitative modelling than data in the more conventional mid-wavenumber range (340–2000 cm⁻¹). Our results point the way to enhanced quantitative analytical capabilities for TRS, with potential application areas including pharmaceuticals, security and process-analytical technology, by combining data sub-selection with low-wavenumber-capable optics.