Quantitative statistical analysis, optimization and noise reduction of atomic resolved electron energy loss spectrum images

K. J. Dudeck; M. Couillard; S. Lazar; C. Dwyer; G. A. Botton

doi:10.1016/j.micron.2011.07.008

Quantitative statistical analysis, optimization and noise reduction of atomic resolved electron energy loss spectrum images

K. J. Dudeck, M. Couillard, S. Lazar, C. Dwyer, G. A. Botton

Physics

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

In this work we investigate methods of statistical processing and background fitting of atomic resolution electron energy loss spectrum image (SI) data. Application of principal component analysis to SI data has been analyzed in terms of the spectral signal-to-noise ratio (SNR) and was found to improve both the spectral SNR and its standard deviation over the SI, though only the latter was found to improve significantly and consistently across all data sets analyzed. The influence of the number of principal components used in the reconstructed data set on the SNR and resultant elemental maps has been analyzed and the experimental results are compared to theoretical calculations.

Original language	English (US)
Pages (from-to)	57-67
Number of pages	11
Journal	Micron
Volume	43
Issue number	1
DOIs	https://doi.org/10.1016/j.micron.2011.07.008
State	Published - Jan 2012

Keywords

EELS
Principal component analysis
Signal-to-noise ratio
Spectrum image
Strontium titanate

ASJC Scopus subject areas

Structural Biology
General Materials Science
General Physics and Astronomy
Cell Biology

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10.1016/j.micron.2011.07.008

Cite this

@article{69609fd10c274ce5bc8040e5b7bfa908,

title = "Quantitative statistical analysis, optimization and noise reduction of atomic resolved electron energy loss spectrum images",

abstract = "In this work we investigate methods of statistical processing and background fitting of atomic resolution electron energy loss spectrum image (SI) data. Application of principal component analysis to SI data has been analyzed in terms of the spectral signal-to-noise ratio (SNR) and was found to improve both the spectral SNR and its standard deviation over the SI, though only the latter was found to improve significantly and consistently across all data sets analyzed. The influence of the number of principal components used in the reconstructed data set on the SNR and resultant elemental maps has been analyzed and the experimental results are compared to theoretical calculations.",

keywords = "EELS, Principal component analysis, Signal-to-noise ratio, Spectrum image, Strontium titanate",

author = "Dudeck, {K. J.} and M. Couillard and S. Lazar and C. Dwyer and Botton, {G. A.}",

note = "Funding Information: The experimental work was carried out at the Canadian Centre for Electron Microscopy, a facility supported by NSERC and McMaster University. KJD acknowledges the Ministry of Research and Innovation of Ontario for financial support. GAB is grateful for NSERC Discovery and Strategic grants for partially supporting this work and an International Strategic Opportunity grant from the Ministry of Research and Innovation of Ontario for supporting the interaction with the Monash Centre for Electron Microscopy at Monash University. CD acknowledges financial support from the Australian Research Council ( DP110104734 ). ",

year = "2012",

month = jan,

doi = "10.1016/j.micron.2011.07.008",

language = "English (US)",

volume = "43",

pages = "57--67",

journal = "Micron",

issn = "0968-4328",

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T1 - Quantitative statistical analysis, optimization and noise reduction of atomic resolved electron energy loss spectrum images

AU - Dudeck, K. J.

AU - Couillard, M.

AU - Lazar, S.

AU - Dwyer, C.

AU - Botton, G. A.

N1 - Funding Information: The experimental work was carried out at the Canadian Centre for Electron Microscopy, a facility supported by NSERC and McMaster University. KJD acknowledges the Ministry of Research and Innovation of Ontario for financial support. GAB is grateful for NSERC Discovery and Strategic grants for partially supporting this work and an International Strategic Opportunity grant from the Ministry of Research and Innovation of Ontario for supporting the interaction with the Monash Centre for Electron Microscopy at Monash University. CD acknowledges financial support from the Australian Research Council ( DP110104734 ).

PY - 2012/1

Y1 - 2012/1

N2 - In this work we investigate methods of statistical processing and background fitting of atomic resolution electron energy loss spectrum image (SI) data. Application of principal component analysis to SI data has been analyzed in terms of the spectral signal-to-noise ratio (SNR) and was found to improve both the spectral SNR and its standard deviation over the SI, though only the latter was found to improve significantly and consistently across all data sets analyzed. The influence of the number of principal components used in the reconstructed data set on the SNR and resultant elemental maps has been analyzed and the experimental results are compared to theoretical calculations.

AB - In this work we investigate methods of statistical processing and background fitting of atomic resolution electron energy loss spectrum image (SI) data. Application of principal component analysis to SI data has been analyzed in terms of the spectral signal-to-noise ratio (SNR) and was found to improve both the spectral SNR and its standard deviation over the SI, though only the latter was found to improve significantly and consistently across all data sets analyzed. The influence of the number of principal components used in the reconstructed data set on the SNR and resultant elemental maps has been analyzed and the experimental results are compared to theoretical calculations.

KW - EELS

KW - Principal component analysis

KW - Signal-to-noise ratio

KW - Spectrum image

KW - Strontium titanate

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U2 - 10.1016/j.micron.2011.07.008

DO - 10.1016/j.micron.2011.07.008

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SN - 0968-4328

VL - 43

SP - 57

EP - 67

JO - Micron

JF - Micron

IS - 1

ER -

Quantitative statistical analysis, optimization and noise reduction of atomic resolved electron energy loss spectrum images

Abstract

Keywords

ASJC Scopus subject areas

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