A new application of X-ray scattering using principal component analysis - classification and identification of liquid precursor chemicals

2012 ◽  
Vol 42 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Yu Zhong ◽  
Fang Zhang ◽  
Wei Li ◽  
Minqiang Li ◽  
Bai Sun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Liquid Precursor ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Applications of principal component analysis to pair distribution function data

2015 ◽  
Vol 48 (6) ◽  
pp. 1619-1626 ◽  
Author(s):  
Karena W. Chapman ◽  
Saul H. Lapidus ◽  
Peter J. Chupas
Keyword(s):  
Principal Component Analysis ◽  
Distribution Function ◽  
Pair Distribution Function ◽  
Principal Component ◽  
Component Analysis ◽  
Scattering Data ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Developments in X-ray scattering instruments have led to unprecedented access toin situand parametric X-ray scattering data. Deriving scientific insights and understanding from these large volumes of data has become a rate-limiting step. While formerly a data-limited technique, pair distribution function (PDF) measurement capacity has expanded to the point that the method is rarely limited by access to quantitative data or material characteristics – analysis and interpretation of the data can be a more severe impediment. This paper shows that multivariate analyses offer a broadly applicable and efficient approach to help analyse series of PDF data from high-throughput andin situexperiments. Specifically, principal component analysis is used to separate features from atom–atom pairs that are correlated – changing concentration and/or distance in concert – allowing evaluation of how they vary with material composition, reaction state or environmental variable. Without requiring prior knowledge of the material structure, this can allow the PDF from constituents of a material to be isolated and its structure more readily identified and modelled; it allows one to evaluate reactions or transitions to quantify variations in species concentration and identify intermediate species; and it allows one to identify the length scale and mechanism relevant to structural transformations.

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 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).

Enhancing the Signal-to-Noise Ratio of X-ray Diffraction Profiles by Smoothed Principal Component Analysis

2005 ◽  
Vol 77 (20) ◽  
pp. 6563-6570 ◽  
Author(s):  
Zeng Ping Chen ◽  
Julian Morris ◽  
Elaine Martin ◽  
Robert B. Hammond ◽  
Xiaojun Lai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray Diffraction ◽  
Signal To Noise ◽  
X Ray ◽  
Noise Ratio

Improvements of Elemental Mapping via X-ray Spectrum Imaging Combined with Principal Component Analysis and Zero-Peak Deconvolution

2004 ◽  
Vol 10 (S02) ◽  
pp. 1040-1041 ◽  
Author(s):  
M. Watanabe ◽  
D.B. Williams
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Elemental Mapping ◽  
X Ray ◽  
Peak Deconvolution ◽  
Spectrum Imaging

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

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