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...

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).

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 EXEMPLIFICANDO O USO DO MACRO MAPEAMENTO ELEMENTAR POR XRF (MA-XRF) NA INVESTIGAÇÃO FORENSE DE OBRAS DE ARTE

Química Nova ◽  
2021 ◽  
Author(s):  
André Pimenta ◽  
Valter Felix ◽  
Daniele Silva ◽  
Marcelo Pereira ◽  
Ana Oliveira ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Image Correlation ◽  
Scanning System ◽  
Elemental Distribution ◽  
Elemental Mapping ◽  
Forensic Investigation ◽  
Multivariate Statistical ◽  
Xrf Scanning ◽  
History Of

EXEMPLIFYING THE USE OF MACRO ELEMENTAL MAPPING BY XRF (MA-XRF) SCANNING IN FORENSIC INVESTIGATION OF ARTWORKS. This work exemplifies the potential of employing macro elemental mapping by XRF (MA-XRF) scanning in the forensic investigation of artworks. The results of the investigation of two artworks of different styles and periods are presented and discussed. The images provided by MA-XRF scanning have information about the elemental distribution, which is related to pigments used in the artwork. These images bring different information such as: if there are underlying paintings; if a given pigment was used for the purpose of restoration or in the creation. Therefore, through the images, it is possible to obtain information, which refers to the process of creating the artwork and the history of conservation and restoration. Through the results, it was possible to verify that the two artworks are probably false because they present anachronistic pigments with the supposed period of creation. The analyzes were performed using a portable MA-XRF scanning system, and in addition to presenting the utility of the instrument in these investigations, the work also presents possibilities to explore the MA-XRF data through multivariate statistical analysis and image correlation.

Relationship Between Major And Trace Elements In Ulaanbaatar Soils: A Study Based On Multivariate Statistical Analysis

2019 ◽  
Vol 12 (3) ◽  
pp. 199-212 ◽  
Author(s):  
Elena V. Shabanova ◽  
Ts. Byambasuren ◽  
G. Ochirbat ◽  
Irina E. Vasil'eva ◽  
B. Khuukhenkhuu ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Analysis ◽  
Principal Components ◽  
Multivariate Statistical Analysis ◽  
Urban Soils ◽  
Thermal Power ◽  
Principal Component ◽  
Major Elements ◽  
Multivariate Statistical ◽  
Data Set

This article focuses on the relationships between major (Si, Al, Mg, Fe, Ca, Na, K, S, P and Ti) and potentially toxic trace (Ag, As, B, Ba, Bi, Co, Cd, Cr, Cu, F, Ge, Mo, Mn, Li, Ni, Pb, Sb, Sn, Sr, Tl, V and Zn) elements in Ulaanbaatar surface soils and also sources of the trace elements in the soils distinguished by the methods of multivariate statistical analysis. Results of exploratory data analysis of 325 Ulaanbaatar soil samples show the accumulation of Ca, S, B, Bi, Cu, Mo, Pb, Sb, Sn, Sr and Zn in urban soils. The major elements were grouped by cluster analysis in tree associations characterizing main soil fractions: sandy P-(K-Na-Si), clayey (Mg-Ti-Fe-Al) and silty (S-Ca). The factor analysis shows that silty fraction is enriched in major elements of both natural and anthropogenic origin. The principal component analysis from 32 variables extracted nine principal components with 82.49% of the cumulative explained variance. The results of cluster and factor analyses well agree and reaffirm the enrichment causes of potentially toxic elements are a coal combustion at thermal power stations (B, Bi, Ca, Mo, S and Sr) and traffic emissions (Cu, Pb, Sn and Zn). Spatial distributions of trace elements in the districts of Ulaanbaatar city were obtained by ordinary kriging. It is illustrated that the different principal components define the various origins and patterns of accumulation of trace elements in soils. The supplementation of data set by the concentration of organic carbon and the species of elements could help to identify the sources of such elements as P, Ni, Al, Fe, Ca, Ba, Bi, Cr, Zn, Sr and Sb in urban soils more completely.

Quantitative Chemical Mapping with Low-Voltage EDS Spectrum Imaging and Multivariate Statistical Analysis (MSA)

1999 ◽  
Vol 5 (S2) ◽  
pp. 318-319 ◽  
Author(s):  
I.M. Anderson
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Multivariate Statistical Analysis ◽  
Low Voltage ◽  
Chemical Mapping ◽  
Multivariate Statistical ◽  
Full Spectrum ◽  
Significant Information ◽  
X Ray ◽  
Spectrum Imaging

One of the advantages of performing X-ray microanalysis at low (≤5 kV) operating voltages is that high spatial resolution (≤250 nm) chemical maps of the specimen can be acquired. Spectrum imaging, where a full spectrum is acquired for each pixel in a two-dimensional array, provides the most comprehensive method of characterization, as long as the sampling density (pixel size) is sufficiently smaller than the spatial resolution. Multivariate statistical analysis (MSA) methods are effective in reducing the large (typically ∼10 MByte) raw spectrum images to the modest (typically ∼100 kByte) data files that contain all of the statistically significant information of interest about the specimen. Preliminary analysis of a cross section of a computer chip from a major semiconductor company was previously performed using limited spectrum imaging capabilities available with the 4pi X-ray mapping module, which allowed for simultaneous acquisition of only 48 channels. MSA of images acquired with only a 960 eV portion of the spectrum containing the Al-K, Si-K and W-M lines showed that excellent contrast between the Si- and W-rich regions of the specimen could be achieved in spite of the strong overlap between Si-Kα and W-Mα (∼34 eV separation).

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.

Sign in / Sign up

Export Citation Format

Share Document