X-ray absorption spectroscopy of laser-produced plasmas: A study of the experiment and data analysis

1989 ◽  
Vol 40 (1) ◽  
pp. 330-340 ◽  
Author(s):  
J. Balmer ◽  
C. L. S. Lewis ◽  
R. E. Corbett ◽  
E. Robertson ◽  
S. Saadat ◽  
...  
Keyword(s):  
Data Analysis ◽  
Absorption Spectroscopy ◽  
X Ray ◽  
X Ray Absorption

Estimating the number of pure chemical components in a mixture by X-ray absorption spectroscopy

2014 ◽  
Vol 21 (5) ◽  
pp. 1140-1147 ◽  
Author(s):  
Alain Manceau ◽  
Matthew Marcus ◽  
Thomas Lenoir
Keyword(s):  
Data Analysis ◽  
Absorption Spectroscopy ◽  
Principal Component ◽  
Weighted Sums ◽  
Chemical Components ◽  
F Test ◽  
X Ray ◽  
Number Of Components ◽  
Pure Compounds ◽  
X Ray Absorption

Principal component analysis (PCA) is a multivariate data analysis approach commonly used in X-ray absorption spectroscopy to estimate the number of pure compounds in multicomponent mixtures. This approach seeks to describe a large number of multicomponent spectra as weighted sums of a smaller number of component spectra. These component spectra are in turn considered to be linear combinations of the spectra from the actual species present in the system from which the experimental spectra were taken. The dimension of the experimental dataset is given by the number of meaningful abstract components, as estimated by the cascade or variance of the eigenvalues (EVs), the factor indicator function (IND), or the F-test on reduced EVs. It is shown on synthetic and real spectral mixtures that the performance of the IND and F-test critically depends on the amount of noise in the data, and may result in considerable underestimation or overestimation of the number of components even for a signal-to-noise (s/n) ratio of the order of 80 (σ = 20) in a XANES dataset. For a given s/n ratio, the accuracy of the component recovery from a random mixture depends on the size of the dataset and number of components, which is not known in advance, and deteriorates for larger datasets because the analysis picks up more noise components. The scree plot of the EVs for the components yields one or two values close to the significant number of components, but the result can be ambiguous and its uncertainty is unknown. A new estimator, NSS-stat, which includes the experimental error to XANES data analysis, is introduced and tested. It is shown that NSS-stat produces superior results compared with the three traditional forms of PCA-based component-number estimation. A graphical user-friendly interface for the calculation of EVs, IND, F-test and NSS-stat from a XANES dataset has been developed under LabVIEW for Windows and is supplied in the supporting information. Its possible application to EXAFS data is discussed, and several XANES and EXAFS datasets are also included for download.

Simulating the XANES of metalloenzymes – a case study

2002 ◽  
Vol 10 (1) ◽  
pp. 64-68 ◽  
Author(s):  
Ana Mijovilovich ◽  
Wolfram Meyer-Klaucke
Keyword(s):  
Data Analysis ◽  
Absorption Spectroscopy ◽  
Theory Development ◽  
Structural Models ◽  
Spectroscopy Data ◽  
X Ray ◽  
Catalytic Mechanisms ◽  
X Ray Absorption ◽  
Future Theory

The analysis of XANES patterns is very indicative for screening samples. Powerful X-ray absorption spectroscopy data-analysis programs can simulate these patterns. Here, a case study on two structural motifs is presented: a non-heme Fe site (2-His-1-carboxylate motif) and the metallo β-lactamase dinuclear Zn site. Simulations of the edge shapes for different structural models will be compared with experimental results, pointing out limitations and challenges. The influence of single neighbouring atoms in the first and second shell on the resulting XANES pattern is discussed. Insights into catalytic mechanisms and the requirements for future theory development are addressed.

Local Structure Studies of Ti for SrTi16O3and SrTi18O3by Advanced X-ray Absorption Spectroscopy Data Analysis

2015 ◽  
Vol 485 (1) ◽  
pp. 42-52 ◽  
Author(s):  
A. Anspoks ◽  
J. Timoshenko ◽  
D. Bocharov ◽  
J. Purans ◽  
F. Rocca ◽  
...  
Keyword(s):  
Data Analysis ◽  
Absorption Spectroscopy ◽  
Local Structure ◽  
Spectroscopy Data ◽  
X Ray ◽  
X Ray Absorption
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