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Proceedings Paper

Raman chemical imaging using high-speed curve resolution chemometrics
Author(s): Thomas M. Hancewicz; Jeremy J. Andrew
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Paper Abstract

The relationship between chemical composition and microstructure is becoming increasingly more important in industry as a way of identifying key performance related properties of fully formulated products. Raman micro- spectroscopic imaging has become the preferred method of obtaining this type of information due to the wealth of detail inherent in Raman spectra pertaining to both microstructure and chemical composition. Concurrently, rapid chemometric data elucidation methodology is required to be able to simultaneously extract pertinent chemical and concentration information in a self-modeling manner from these typically huge and complex data sets. Examples of Raman micro-spectroscopic imaging will be presented as a demonstration of how the principles of Raman imaging can be applied to complex, multi-component, multi-phase systems of inherently low contrast. The use of two-way multivariate curve resolution (MCR) methodology is described as a means of rapidly processing the extremely large, three-way Raman chemical images, dramatically simplifying data analysis. The results from MCR analysis provide the number of chemical species present in the sample, the spectrum of each species for identification, and the concentration image for each species. The additional benefit of image noise reduction is also described for the MCR approach. A comparison of results is given describing the two-way PFA methods currently in use and how they compare with MCR methodology.

Paper Details

Date Published: 9 June 1998
PDF: 12 pages
Proc. SPIE 3261, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V, (9 June 1998); doi: 10.1117/12.310569
Show Author Affiliations
Thomas M. Hancewicz, Unilever Research US (United States)
Jeremy J. Andrew, Unilever Research US (United Kingdom)


Published in SPIE Proceedings Vol. 3261:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V
Thomas Taiwei Lu; Carol J. Cogswell; Jeremy M. Lerner; Jose-Angel Conchello; Jeremy M. Lerner; Thomas Taiwei Lu; Tony Wilson, Editor(s)

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