Multivariate Statistical Analysis of Wavelength and Energy-Dispersive X-ray Spectral Images

2004 ◽  
Vol 10 (S02) ◽  
pp. 118-119 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan ◽  
Richard P. Grant ◽  
Paul F. Hlava
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Energy Dispersive ◽  
Multivariate Statistical ◽  
X Ray

Extended abstract of a paper presented at Microscopy and Microanalysis 2004 in Savannah, Georgia, USA, August 1–5, 2004.

A Standards-Based Method for Compositional Analysis by Energy Dispersive X-Ray Spectrometry Using Multivariate Statistical Analysis: Application to Multicomponent Alloys

2013 ◽  
Vol 19 (1) ◽  
pp. 66-72
Author(s):  
Monika Rathi ◽  
S.P. Ahrenkiel ◽  
J.J. Carapella ◽  
M.W. Wanlass
Keyword(s):  
Statistical Analysis ◽  
Elemental Composition ◽  
Multivariate Statistical Analysis ◽  
Reference Standards ◽  
Multicomponent Alloy ◽  
Energy Dispersive ◽  
Foil Thickness ◽  
Atomic Fraction ◽  
Multivariate Statistical ◽  
X Ray

AbstractGiven an unknown multicomponent alloy, and a set of standard compounds or alloys of known composition, can one improve upon popular standards-based methods for energy dispersive X-ray (EDX) spectrometry to quantify the elemental composition of the unknown specimen? A method is presented here for determining elemental composition of alloys using transmission electron microscopy–based EDX with appropriate standards. The method begins with a discrete set of related reference standards of known composition, applies multivariate statistical analysis to those spectra, and evaluates the compositions with a linear matrix algebra method to relate the spectra to elemental composition. By using associated standards, only limited assumptions about the physical origins of the EDX spectra are needed. Spectral absorption corrections can be performed by providing an estimate of the foil thickness of one or more reference standards. The technique was applied to III-V multicomponent alloy thin films: composition and foil thickness were determined for various III-V alloys. The results were then validated by comparing with X-ray diffraction and photoluminescence analysis, demonstrating accuracy of approximately 1% in atomic fraction.

scholarly journals Chemical Species, Micromorphology, and XRD Fingerprint Analysis of Tibetan MedicineZuotaiContaining Mercury

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Cen Li ◽  
Hongxia Yang ◽  
Yuzhi Du ◽  
Yuancan Xiao ◽  
Zhandui ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Chemical Species ◽  
Physicochemical Characteristics ◽  
Chemical Substance ◽  
Tibetan Medicine ◽  
Fingerprint Analysis ◽  
Multivariate Statistical ◽  
Positive Role ◽  
X Ray

Zuotai(gTso thal) is one of the famous drugs containing mercury in Tibetan medicine. However, little is known about the chemical substance basis of its pharmacodynamics and the intrinsic link of different samples sources so far. Given this, energy dispersive spectrometry of X-ray (EDX), scanning electron microscopy (SEM), atomic force microscopy (AFM), and powder X-ray diffraction (XRD) were used to assay the elements, micromorphology, and phase composition of nineZuotaisamples from different regions, respectively; the XRD fingerprint features ofZuotaiwere analyzed by multivariate statistical analysis. EDX result shows thatZuotaicontains Hg, S, O, Fe, Al, Cu, and other elements. SEM and AFM observations suggest thatZuotaiis a kind of ancient nanodrug. Its particles are mainly in the range of 100–800 nm, which commonly further aggregate into 1–30 μm loosely amorphous particles. XRD test shows thatβ-HgS, S8, andα-HgS are its main phase compositions. XRD fingerprint analysis indicates that the similarity degrees of nine samples are very high, and the results of multivariate statistical analysis are broadly consistent with sample sources. The present research has revealed the physicochemical characteristics ofZuotai, and it would play a positive role in interpreting this mysterious Tibetan drug.

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.

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 Multivariate Statistical Analysis of Combined Hyperspectral Cathodoluminescence and X-Ray Datasets

2006 ◽  
Vol 12 (S02) ◽  
pp. 1522-1523
Author(s):  
PR Edwards ◽  
RW Martin ◽  
MR Lee
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
X Ray

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2005

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.

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