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.