scholarly journals X-ray spectral variability of blazars using principal component analysis

2018 ◽  
Vol 480 (2) ◽  
pp. 1999-2010 ◽  
Author(s):  
D Gallant ◽  
L C Gallo ◽  
M L Parker
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Spectral Variability ◽  
X Ray

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.

X-ray powder diffractometry in combination with principal component analysis – A tool for monitoring solid state changes

2011 ◽  
Vol 43 (4) ◽  
pp. 278-289 ◽  
Author(s):  
Karin Kogermann ◽  
Peep Veski ◽  
Jukka Rantanen ◽  
Kaisa Naelapää
Keyword(s):  
Principal Component Analysis ◽  
Solid State ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray ◽  
State Changes

Pattern Recognition on X-ray Fluorescence Records from Copenhagen Lake Sediments Using Principal Component Analysis

2014 ◽  
Vol 225 (12) ◽  
Author(s):  
Norman Schreiber ◽  
Emanuel Garcia ◽  
Aart Kroon ◽  
Peter C. Ilsøe ◽  
Kurt H. Kjær ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Lake Sediments ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray
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