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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

DOI

10.1039/c3an01293j

Type

Journal

The Analyst

Publication Date

01/2014

Volume

139

Pages

74 - 78

Addresses

School of Pharmacy, Univeristy of Nottingham, Boots Science Building, NG7 2RD, UK. jonathan.burley@nottingham.ac.uk.

Keywords

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