Application of principal component analysis to X-ray photoelectron spectroscopy — the role of noise in the spectra

1997 ◽  
Vol 83 (2-3) ◽  
pp. 143-150 ◽  
Author(s):  
Murali Sastry
Keyword(s):  
Principal Component Analysis ◽  
Photoelectron Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray

Three-Dimensional X-Ray Photoelectron Tomography on the Nanoscale: Limits of Data Processing by Principal Component Analysis

2013 ◽  
Vol 19 (3) ◽  
pp. 751-760 ◽  
Author(s):  
Shaaker Hajati ◽  
John Walton ◽  
Sven Tougaard
Keyword(s):  
Principal Component Analysis ◽  
Noise Reduction ◽  
Photoelectron Spectroscopy ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Acquisition Time ◽  
Silicon Sample ◽  
Xps Spectra ◽  
X Ray

AbstractIn a previous article, we studied the influence of spectral noise on a new method for three-dimensional X-ray photoelectron spectroscopy (3D XPS) imaging, which is based on analysis of the XPS peak shape [Hajati, S., Tougaard, S., Walton, J. & Fairley, N. (2008). Surf Sci602, 3064–3070]. Here, we study in more detail the influence of noise reduction by principal component analysis (PCA) on 3D XPS images of carbon contamination of a patterned oxidized silicon sample and on 3D XPS images of Ag covered by a nanoscale patterned octadiene layer. PCA is very efficient for noise reduction, and using only the three most significant PCA factors to reconstruct the spectra restores essentially all physical information in both the intensity and shape of the XPS spectra. The corresponding signal-to-noise improvement was estimated to be equivalent to a reduction by a factor of 200 in the required data acquisition time. A small additional amount of information is obtained by using up to five PCA factors, but due to the increased noise level, this information can only be extracted if the intensity of the start and end points for each spectrum are obtained as averages over several energy points.

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 A-49 Visual Memory Errors and Intelligence: The Role of Verbal Mediation

2019 ◽  
Vol 34 (6) ◽  
pp. 908-909
Author(s):  
K Hakinson ◽  
J Moses ◽  
J RIvera ◽  
A Guerra ◽  
M Davis ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Perceptual Organization ◽  
Visual Memory ◽  
Principal Component ◽  
Component Analysis ◽  
Spoken Language ◽  
Verbal Mediation ◽  
Memory Errors ◽  
Shared Variance

Abstract Objective Examine the relationship of verbal mediation with visual memory errors and intelligence to understand the role of spoken language on other assessment measures. Method Assessment records were obtained from a Veteran Affairs clinic for veterans (n=100) with diverse neuropsychiatric conditions who completed the Wechsler Adult Intelligence Scale, third edition (WAIS-III), Multilingual Aphasia Examination (MAE), and Benton Visual Retention Test (BVRT). A Principal Component Analysis (PCA) was used to examine the interrelationship among these assessments. The components of spoken language, types of errors on the BVRT, and the four factors of the WAIS-III were factored using the PCA to identify common sources of variance. Results A principal component analysis revealed a six-factor model explaining 68.16% of the shared variance among the WAIS-III factors, MAE components, and BVRT Errors. Omission errors loaded with Processing Speed and Controlled Word Association. Distortions and size errors loaded with Perceptual Organization. Size errors also loaded with Verbal Comprehension and Visual Naming. Misplacements loaded with Working Memory and Sentence Repetition. Misplacements, perseverations, and omissions loaded with the Token Test (a measure associated with auditory comprehension). Rotation errors loaded with Perceptual Organization. Conclusions Results indicated significant shared variance between visual memory errors, spoken language, and intelligence factors. This suggests that spoken language is involved in the process of visual memory, and deficits in spoken language may result in increased errors on visual memory tasks. Therefore, treatment recommendations for visual memory difficulties should take into consideration verbal capabilities and intelligence factors to better individualize treatment.

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
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