Vanadium K-Edge X-ray Absorption Spectroscopy as a Probe of the Heterogeneous Lithiation of V2O5: First-Principles Modeling and Principal Component Analysis

2016 ◽  
Vol 120 (42) ◽  
pp. 23922-23932 ◽  
Author(s):  
Gregory A. Horrocks ◽  
Erick J. Braham ◽  
Yufeng Liang ◽  
Luis R. De Jesus ◽  
Joshua Jude ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Absorption Spectroscopy ◽  
First Principles ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray ◽  
X Ray Absorption

scholarly journals XAS studies of Se speciation in selenite-fed rats

Metallomics ◽  
2014 ◽  
Vol 6 (12) ◽  
pp. 2193-2203 ◽  
Author(s):  
Claire M. Weekley ◽  
Jade B. Aitken ◽  
Paul K. Witting ◽  
Hugh H. Harris
Keyword(s):  
Principal Component Analysis ◽  
Absorption Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Selenium Speciation ◽  
X Ray ◽  
X Ray Absorption ◽  
Se Speciation

An investigation of selenium speciation in the tissues of selenite-fed rats by principal component analysis of X-ray absorption spectra.

An energy dispersive X-ray fluorescence spectrometry approach for the identification of geographical origin of wheat flour

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Zaiyong Si ◽  
Qianwei Cheng ◽  
Hui Chen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Flour ◽  
Principal Component ◽  
Component Analysis ◽  
Mineral Elements ◽  
Energy Dispersive ◽  
Fluorescence Spectrometry ◽  
Energy Channel ◽  
X Ray ◽  
Different Origins

Abstract In this work, a method was established for discriminating geographical origins of wheat flour based on energy dispersive X-ray fluorescence spectrometry (ED-XRF) and chemometrics. 68 wheat flour samples from three different origins were collected and analyzed using ED-XRF technology. Firstly, the principal component analysis method was applied to analyze the feasibility of discrimination and reduce data dimensionality. Then, Competitive Adaptive Reweighted Sampling (CARS) was used to further extract feature variables, and 12 energy variables (corresponding to mineral elements) were identified and selected to characterize the geographical attributes of wheat flour samples. Finally, a non-linear model was constructed using principal component analysis and quadratic discriminant analysis (QDA). The CARS-PCA-QDA model showed that the accuracy of five-fold cross-validation was 84.25%. The results showed that the established method was able to select important energy channel variables effectively and wheat flour could be classified based on geographical origins with chemometrics, which could provide a theoretical basis for unveiling the relationship between mineral element composition and wheat origin.

scholarly journals New Insights on the Reversible Lithiation Mechanism of TiO2(B) by Operando X-ray Absorption Spectroscopy and X-ray Diffraction Assisted by First-Principles Calculations

2014 ◽  
Vol 118 (47) ◽  
pp. 27210-27218 ◽  
Author(s):  
Marcus Fehse ◽  
Mouna Ben Yahia ◽  
Laure Monconduit ◽  
Frédéric Lemoigno ◽  
Marie-Liesse Doublet ◽  
...  
Keyword(s):  
Absorption Spectroscopy ◽  
First Principles ◽  
First Principles Calculations ◽  
X Ray Diffraction ◽  
X Ray ◽  
X Ray Absorption

Plastic Classification with X-ray Absorption Spectroscopy Based on Back Propagation Neural Network

2017 ◽  
Vol 71 (11) ◽  
pp. 2538-2548 ◽  
Author(s):  
Qian Wang ◽  
Xiaomei Wu ◽  
Lingcong Chen ◽  
Zheng Yang ◽  
Zheng Fang
Keyword(s):  
Absorption Spectroscopy ◽  
Recognition Rate ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
X Ray ◽  
Analysis Methods ◽  
Average Recognition Rate ◽  
The Stability ◽  
X Ray Absorption

Currently, spectral analysis methods used in the classification of plastics have limitations that do not apply to opaque plastics or the stability of experimental results is not strong. In this paper, X-ray absorption spectroscopy (XAS) has been applied to classify plastics due to its strong penetrability and stability. Fifteen kinds of plastics are selected as specimens. X-ray, which is excited by a voltage of 60 kV, penetrated these specimens. The spectral data acquired by CdTe X-ray detector are processed by principal component analysis (PCA) and other data analysis methods. Then the back propagation neural networks (BPNN) algorithm is used to classify the processed data. The average recognition rate reached 96.95% and classification results of all types of plastic results were analyzed in detail. It indicates that XAS has the potential to classify plastics and that XAS can be used in some fields such as plastic waste sorting and recycling. At the same time, the technology of XAS, in the future, can also be used to classify more substances.

scholarly journals Principal Component Analysis of Multiwavelength Properties of Seyfert Galaxies

1994 ◽  
Vol 159 ◽  
pp. 502-502
Author(s):  
Deborah Dultzin–Hacyan ◽  
Carlos Ruano
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Seyfert Galaxies ◽  
Component Analysis ◽  
X Ray ◽  
Radio Data

A multidimensional statistical analysis of observed properties of Seyfert galaxies has been carried out using Principal Component Analysis (PCA) applied to X-ray, optical, near and far IR and radio data for all the Seyfert galaxies types 1 and 2 for the catalog by Lipovtsky et al. (1987).

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