Abstract
Annual Review of Physical Chemistry
Vol. 56:
429-474
(Volume publication date May 2005)
(doi:10.1146/annurev.physchem.56.092503.141205)
First published online as a Review in Advance on January 7, 2005FOURIER TRANSFORM INFRARED VIBRATIONAL SPECTROSCOPIC IMAGING: Integrating Microscopy and Molecular Recognition *Ira W. Levin and Rohit Bhargava Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-0520; email: iwl@helix.nih.gov, rb319b@nih.gov ▪ Abstract The recent development of Fourier transform infrared (FTIR) spectroscopic imaging has enhanced our capability to examine, on a microscopic scale, the spatial distribution of vibrational spectroscopic signatures of materials spanning the physical and biomedical disciplines. Recent activity in this emerging area has concentrated on instrumentation development, theoretical analyses to provide guidelines for imaging practice, novel data processing algorithms, and the introduction of the technique to new fields. To illustrate the impact and promise of this spectroscopic imaging methodology, we present fundamental principles of the technique in the context of FTIR spectroscopy and review new applications in various venues ranging from the physical chemistry of macromolecular systems to the detection of human disease. Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy Analytical and Bioanalytical Chemistry 391(5):1641-1654 (2008) Raman and FTIR microscopic imaging of colon tissue: a comparative study Journal of Biophotonics 1(2):154-169 (2008) Utilization of spectral vector properties in multivariate chemometrics analysis of hyperspectral infrared imaging data for cellular studies The Analyst 133(10):1395 (2008) Time-resolving molecular vibration for microanalytics: single laser beam nonlinear Raman spectroscopy in simulation and experiment Physical Chemistry Chemical Physics 10(5):681 (2008) Observer-invariant histopathology using genetics-based machine learning
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