Multivariate Statistical Analysis of Three-Spatial-Dimension TOF-SIMS Raw Data Sets

2007 ◽  
Vol 79 (20) ◽  
pp. 7719-7726 ◽  
Author(s):  
V. S. Smentkowski ◽  
S. G. Ostrowski ◽  
E. Braunstein ◽  
M. R. Keenan ◽  
J. A. (Tony) Ohlhausen ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Spatial Dimension ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Raw Data ◽  
Tof Sims

scholarly journals Multivariate Statistical Analysis of Spectrum Lines and Images

1997 ◽  
Vol 3 (S2) ◽  
pp. 931-932 ◽  
Author(s):  
Ian M. Anderson ◽  
Jim Bentley
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quantitative Imaging ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Recent Developments ◽  
Data Files

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

scholarly journals Multivariate Statistical Analysis of Particle X-ray Spectra

1998 ◽  
Vol 4 (S2) ◽  
pp. 202-203
Author(s):  
Ian M. Anderson ◽  
John A. Small
Keyword(s):  
Statistical Analysis ◽  
Trace Element ◽  
Multivariate Statistical Analysis ◽  
A Priori ◽  
Detectable Level ◽  
Multivariate Statistical ◽  
Raw Data ◽  
X Ray ◽  
Carbon Substrates ◽  
Particle Spectra

Multivariate statistical analysis (MSA) is a powerful tool for the analysis of series of spectra. This paper explores an application of MSA to a series of energy dispersive X-ray (EDX) spectra acquired in the scanning electron microscope (SEM) from a series of particles. The raw data were series of spectra previously acquired to test analytical procedures for trace element detection. This paper explores the possibility of performing the trace element detection with MSA components that have been extracted from the raw data without any a priori assumptions about the information content of the particle spectra. Particles were prepared from two analytical glasses, dispersed onto carbon substrates and coated with carbon. The compositions of the two glasses are substantially similar, except that one glass (K-3106) contains 0.7 wt.% Fe, whereas the other glass (K-3069) does not contain Fe at a detectable level.

scholarly journals Discrimination of white ginseng origins using multivariate statistical analysis of data sets

2014 ◽  
Vol 38 (3) ◽  
pp. 187-193 ◽  
Author(s):  
Hyuk-Hwan Song ◽  
Ji Young Moon ◽  
Hyung Won Ryu ◽  
Bong-Soo Noh ◽  
Jeong-Han Kim ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
White Ginseng

scholarly journals Distinguishing Monosaccharide Stereo- and Structural Isomers with TOF-SIMS and Multivariate Statistical Analysis

2006 ◽  
Vol 78 (18) ◽  
pp. 6497-6503 ◽  
Author(s):  
Elena S. F. Berman ◽  
Kristen S. Kulp ◽  
Mark G. Knize ◽  
Ligang Wu ◽  
Erik J. Nelson ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Structural Isomers ◽  
Multivariate Statistical ◽  
Tof Sims

scholarly journals A Complete Characterization of Samples Using Multivariate Statistical Analysis of 3Dimensional MCs+ ToF-SIMS Data

2011 ◽  
Vol 17 (S2) ◽  
pp. 1462-1463
Author(s):  
V Smentkowski ◽  
M Keenan
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Complete Characterization ◽  
Multivariate Statistical ◽  
Tof Sims

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Multivariate statistical analysis of non-mass-selected ToF-SIMS data

2008 ◽  
Vol 40 (8) ◽  
pp. 1176-1182 ◽  
Author(s):  
V. S. Smentkowski ◽  
S. G. Ostrowski ◽  
F. Kollmer ◽  
A. Schnieders ◽  
M. R. Keenan ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
Tof Sims

Towards The Automatic Segmentation of Multiple Micro Analytical Maps

1997 ◽  
Vol 3 (S2) ◽  
pp. 929-930
Author(s):  
N. Bonnet
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Automatic Segmentation ◽  
Data Sets ◽  
Sources Of Information ◽  
Multivariate Statistical ◽  
Data Set ◽  
Analytical Instruments ◽  
Different Sources

Multi-dimensional data sets are now produced by many analytical instruments. They include the series of spectra, the series of images and spectrum-images, which can be considered as a series of spectra at different positions or series of images at different wavelengths.The automatic (or semi-automatic) handling of such data sets requires that new multivariate analysis methods are made available. For instance, if we restrict ourselves to image sets, there is a need to deduce (from the multiple maps) a single map in which regions of the specimen with approximate homogeneous properties (composition ...) can be identified and quantified.At the present time, only a limited number of software tools are available for this purpose: - the scatterplot allows the display of the correlations between two or three spectra or images, - Interactive Correlation Partitioning (ICP) allows the user to divide the scatterplot into several parts and to reconstitute images with one selected part, -Multivariate Statistical Analysis (MSA) allows us to analyze a data set composed of several images and to identify the different sources of information, and to filter out noise and experimental artefacts.

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