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Model‐based biological Raman spectral imaging
Author(s) -
ShaferPeltier Karen E.,
Haka Abigail S.,
Motz Jason T.,
Fitzmaurice Maryann,
Dasari Ramachandra R.,
Feld Michael S.
Publication year - 2002
Publication title -
journal of cellular biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.028
H-Index - 165
eISSN - 1097-4644
pISSN - 0730-2312
DOI - 10.1002/jcb.10418
Subject(s) - raman spectroscopy , chemical imaging , spectral imaging , computer science , principal component analysis , artificial intelligence , biological system , range (aeronautics) , pattern recognition (psychology) , hyperspectral imaging , optics , materials science , physics , biology , composite material
Raman spectral imaging is a powerful tool for determining chemical information in a biological specimen. The challenge is to condense the large amount of spectral information into an easily visualized form with high information content. Researchers have applied a range of techniques, from peak‐height ratios to sophisticated models, to produce interpretable Raman images. The purpose of this article is to review some of the more common imaging approaches, in particular principal components analysis, multivariate curve resolution, and Euclidean distance, as well as to present a new technique, morphological modeling. How to best extract meaningful chemical information using each imaging approach will be discussed and examples of images produced with each will be shown. J. Cell. Biochem. Suppl. 39: 125–137, 2002. © 2002 Wiley‐Liss, Inc.