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).

Prospect for HEW Applications of Multivariate Statistical Analysis in Microscopy and Spectroscopy

1990 ◽  
Vol 48 (1) ◽  
pp. 460-461
Author(s):  
N. Bonnet ◽  
E. Simova ◽  
S. Lebonvallet
Keyword(s):  
Electron Microscopy ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Large Data ◽  
List Type ◽  
Elemental Mapping ◽  
Multivariate Statistical ◽  
Significant Information ◽  
Data Set ◽  
Spatial Series

Multivariate Statistical Analysis has been developed in order to process large data sets and to extract from them the significant information.It has been introduced in electron microscopy in order to process image series of macromolecules and to classify individual images into several subsets of the series. This kind of series can be considered as a spatial series.In fact, electron microscopy and spectroscopy are using more and more image sequences in order to be able to access more precisely the content of the object studied. Among the different types of sequences which are already used and which will be used more extensively in the near future, one can mention : —spatial series (several slightly different objects, whose images are to be classified and combined ; or several microanalytical spectra recorded at different places of the object).—time series : in order to study changes induced in the object, one has to record successive images or spectra.—series in energy : one possibility to study the behavior of a specimen is to vary the accelerating voltage, to record several images or spectra and process the whole data set. Another exemple of series in energy is given by energy loss elemental mapping (or Auger elemental mapping) where several energy filtered images must be recorded (below and above the edge of interest) in order to get a “true” elemental map.—more “exotic” series can also be obtained, for instance series obtained by varying the spot size in MEB, series obtained with different configured detectors in STEM...

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.

Application of Multivariate Statistical Analysis to STEM X-ray Spectral Images: Interfacial Analysis in Microelectronics

2006 ◽  
Vol 12 (6) ◽  
pp. 538-544 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Curve Resolution ◽  
Foil Thickness ◽  
Beam Specimen ◽  
Spectral Image ◽  
Multivariate Statistical ◽  
Data Set ◽  
Thickness Change ◽  
X Ray

Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.

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

Multivariate statistical analysis of a series of alchemi spectra

1996 ◽  
Vol 54 ◽  
pp. 550-551 ◽  
Author(s):  
Ian M. Anderson ◽  
J. Bentley
Keyword(s):  
Statistical Analysis ◽  
Grain Boundary ◽  
Multivariate Statistical Analysis ◽  
Spectral Component ◽  
Boundary Segregation ◽  
Grain Boundary Segregation ◽  
Multivariate Statistical ◽  
X Ray ◽  
The Matrix ◽  
Electron Energy Loss

Multivariate statistical analysis (MSA) of a series of spectra or images offers an objective and quantitative way to characterize the features of the spectra that vary in a correlated fashion and to determine the number of independently varying components in the series. For example, in a series of spectra showing grain boundary segregation, there may be only one independently varying spectral component, which signifies an increase in the concentrations of the segregants and a corresponding decrease in the concentrations of some of the matrix constituents. The basis of the MSA method has been outlined by Trebbia and Bonnet, with application to the analysis of electron energy-loss spectrum images. Titchmarsh et al., have applied this analysis to a series of energy dispersive X-ray (EDX) spectra for the study of grain boundary segregation. The present paper illustrates the application of MSA methods to a series of EDX spectra acquired for ALCHEMI analysis. The basic method has been modified slightly for the analysis of ALCHEMI data.

Statistical Analysis Of SEM-EDX Spectrum Images Of A Metal-Ceramic Braze

1999 ◽  
Vol 5 (S2) ◽  
pp. 806-807
Author(s):  
P.G. Kotula ◽  
I.M. Anderson ◽  
J.J. Stephens
Keyword(s):  
Statistical Analysis ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Polycrystalline Alumina ◽  
Polished Cross Section ◽  
Simple Mapping ◽  
Characterization Of Materials

Spectrum imaging, where a complete spectrum is acquired at each pixel in an image, is potentially a very powerful technique for the characterization of materials. The primary challenge to overcome is to extract all of the relevant information from what are typically large data sets that cannot be readily visualized in their entirety. For energy-dispersive X-ray (EDX) spectrum images, simple mapping has typically been done. In this work, the application of multivariate statistical analysis (MSA) to the characterization of a metal/alumina braze by SEM-EDX spectrum images is described.The braze characterized in this work joins polycrystalline alumina and a copper-silver eutectic alloy containing some titanium (an ‘active’ metal). The specimen geometry consisted of a sandwich of two pieces of alumina, two braze layers and a Kovar (primarily Fe, Ni and Co) filler layer in the middle. The entire assembly was heat-treated to bond the interfaces and then a polished cross-section was prepared.

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
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