Analysis of spectroscopic imaging data by fuzzy C-means clustering

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DOIResolve DOI: http://doi.org/10.1021/ac970206r
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TypeArticle
Journal titleAnalytical Chemistry
ISSN0003-2700
1520-6882
Volume69
Issue16
Pages33703374; # of pages: 5
AbstractA novel method of analyzing spectroscopic imaging data is presented. A fuzzy C-means clustering algorithm has been applied to the analysis of near-infrared spectroscopic imaging data acquired with the combination of a CCD camera and a liquid crystal tunable filter. The use of fuzzy C-means clustering dramatically increased the information obtained from near-IR spectroscopic images and allowed for the detection of small subregions of the image that contained novel and unanticipated spectral features, without the need for a priori knowledge of the chemical composition of the sample. Two illustrative samples were analyzed, one comprised of four different inks printed on label paper and the other containing indocyanine green and human blood patches. The regions containing the different constituents were clearly demarcated and their mean spectra determined. The mean spectra of the second sample were shown to match those obtained using a scanning near-IR spectrometer. In addition to probing the spatial and spectral characteristics of the samples, the fuzzy C-means clustering analysis also helped improve the signal-to-noise ratio of the spectra.
Publication date
PublisherAmerican Chemical Society
LanguageEnglish
AffiliationNRC Institute for Biodiagnostics; National Research Council Canada
Peer reviewedYes
NRC number495
NPARC number9148166
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Record identifier4e6a4325-972a-4c61-99bd-9dda5ef540c3
Record created2009-06-25
Record modified2016-10-31
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