title: Multivariate statistical analysis of Raman images of a pharmaceutical tablet.
abstract: This paper describes the application of principal component analysis (PCA) and independent component analysis (ICA) to identify the reference spectra of a pharmaceutical tablet's constituent compounds from Raman spectroscopic data. The analysis shows, first with a simulated data set and then with data collected from a pharmaceutical tablet, that both PCA and ICA are able to identify most of the features present in the reference spectra of the constituent compounds. However, the results suggest that the ICA method may be more appropriate when attempting to identify unknown reference spectra from a sample. The resulting PCA and ICA models are subsequently used to estimate the relative concentrations of the constituent compounds and to produce spatial distribution images of the analyzed tablet. These images provide a visual representation of the spatial distribution of the constituent compounds throughout the tablet. Images associated with the ICA scores are found to be more informative and not as affected by measurement noise as the PCA based score images. The paper concludes with a discussion of the future work that needs to be undertaken for ICA to gain wider acceptance in the applied spectroscopy community.
authors.affiliation: School of Electrical and Electronic Engineering, University of Manchester, UK.
authors.forename: Haisheng
authors.initials: H
authors.lastname: Lin
authors.forename: Ognjen
authors.initials: O
authors.lastname: Marjanović
authors.forename: Barry
authors.initials: B
authors.lastname: Lennox
authors.forename: Slobodan
authors.initials: S
authors.lastname: Šašić
authors.forename: Ian M
authors.initials: IM
authors.lastname: Clegg
mesh_terms: D002626:Chemistry, Pharmaceutical / Q000379:methods*; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D013059:Spectrum Analysis, Raman / Q000379:methods*; D013607:Tablets / Q000737:chemistry*
chemical_list: D013607:Tablets