Coded aperture optimization in X-ray tomosynthesis via sparse principal component analysis

2020 ◽  
Author(s):  
Tianyi Mao ◽  
Xu Ma ◽  
Angela Cuadros ◽  
Gonzalo Arce
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Coded Aperture ◽  
Sparse Principal Component Analysis ◽  
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.

Integrative sparse principal component analysis

2018 ◽  
Vol 166 ◽  
pp. 1-16 ◽  
Author(s):  
Kuangnan Fang ◽  
Xinyan Fan ◽  
Qingzhao Zhang ◽  
Shuangge Ma
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sparse Principal Component Analysis

scholarly journals Approximation bounds for sparse principal component analysis

2014 ◽  
Vol 148 (1-2) ◽  
pp. 89-110 ◽  
Author(s):  
Alexandre d’Aspremont ◽  
Francis Bach ◽  
Laurent El Ghaoui
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sparse Principal Component Analysis
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