Multivariate data analysis for Raman spectroscopic imaging

  • Shinzawa H
  • Awa K
  • Kanematsu W
 et al. 
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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.

Author-supplied keywords

  • Chemometrics
  • Principal component analysis
  • Raman imaging
  • Self-modeling curve resolution

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  • Hideyuki Shinzawa

  • Kimie Awa

  • Wataru Kanematsu

  • Yukihiro Ozaki

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