Multivariate data analysis for Raman spectroscopic imaging

TLDR

Raman spectra contain key information that is broadly distributed throughout the dataset. This article reviews analytic techniques for Raman spectroscopic imaging with an emphasis on chemometrics. The authors condense the distributed spectral information into compact matrix representations using chemometric methods such as principal component analysis and self‑modeling curve resolution, producing scores and loadings matrices or chemically interpretable SMCR models. These techniques yield insights that facilitate mechanistic modeling of complex Raman phenomena and are illustrated through applications to pharmaceutical tablet imaging. © 2009 John Wiley & Sons, Ltd.

Abstract

Abstract This article reviews the analytic techniques for Raman spectroscopic imaging with emphasis on chemometrics. Key information included in Raman spectra is often distributed broadly throughout the dataset. It is possible to condense the information into a very compact matrix representation by a chemometric technique of factor analysis such as principal component analysis (PCA) or self‐modeling curve resolution (SMCR). PCA yields two matrices called scores and loadings which complementarily represent the entire features broadly distributed in the dataset. This concept can be further extended to other forms of data transformation schemes, including bilinear data decomposition based on SMCR analysis. SMCR offers a firmer model which is chemically or physically interpretable. The information derived from these techniques readily brings useful insight into building a mechanistic model for understanding complex phenomena studied by Raman spectroscopy. Illustrative examples are given for applications of both PCA and SMCR to Raman imaging of pharmaceutical tablets. Copyright © 2009 John Wiley & Sons, Ltd.