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Proceedings Paper

Applications of principal components analyses to multidimensional FTIR microscopy data
Author(s): Kenneth J. Ward; John A. Reffner; Pamela A. Martoglio
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Paper Abstract

The acquisition of multidimensional data, both multispatial and multispectral data, is now routinely accomplished using an FT-IR microscope equipped with a motorized stage. FT-IR microscope mapping generates multi-megabyte data sets with several thousand data points per spectrum, where each spectrum is a pixel in an image. Methods to reduce each infrared spectrum to a single intensity must be used to produce a pseudo 3-D image. Multivariate statistical methods such as principle components analysis (PCA) utilize the multiwavelength information acquired at each spatial location to generate this image containing new chemical information. PCA generates the image by determining independent sources of spectral variance without any knowledge of chemical composition. Since PCA can be applied as a full spectrum method, there is no requirement for any previous knowledge about the data set as is the case for other methods of data reduction.

Paper Details

Date Published: 31 January 1994
PDF: 2 pages
Proc. SPIE 2089, 9th International Conference on Fourier Transform Spectroscopy, (31 January 1994); doi: 10.1117/12.166798
Show Author Affiliations
Kenneth J. Ward, Nicolet Instrument Corp. (United States)
John A. Reffner, Spectra-Tech, Inc. (United States)
Pamela A. Martoglio, Spectra-Tech, Inc. (United States)

Published in SPIE Proceedings Vol. 2089:
9th International Conference on Fourier Transform Spectroscopy
John E. Bertie; Hal Wieser, Editor(s)

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