Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication





The Analyst

Publication Date





74 - 78


School of Pharmacy, Univeristy of Nottingham, Boots Science Building, NG7 2RD, UK.


Pharmaceutical Preparations, Spectrum Analysis, Raman, Multivariate Analysis, Databases, Factual