Document Detail

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy.
MedLine Citation:
PMID:  19417475     Owner:  NLM     Status:  MEDLINE    
An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.
Stephen Jesse; Sergei V Kalinin
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2009-02-03
Journal Detail:
Title:  Nanotechnology     Volume:  20     ISSN:  1361-6528     ISO Abbreviation:  Nanotechnology     Publication Date:  2009 Feb 
Date Detail:
Created Date:  2009-05-06     Completed Date:  2009-06-16     Revised Date:  2010-03-11    
Medline Journal Info:
Nlm Unique ID:  101241272     Medline TA:  Nanotechnology     Country:  England    
Other Details:
Languages:  eng     Pagination:  085714     Citation Subset:  IM    
The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
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MeSH Terms
Data Interpretation, Statistical
Image Interpretation, Computer-Assisted / methods*
Materials Testing / methods*
Microscopy, Scanning Probe / methods*
Nanostructures / chemistry*,  ultrastructure*
Principal Component Analysis

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