Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

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 Use of the Optically Stimulated Luminescence Dating and X-Ray Fluorescence Spectrometry Methods as Tools in Paleoseismology

2019 ◽  
Vol 22 ◽  
pp. 107
Author(s):  
L. M. Tsodoulos ◽  
K. Stamoulis ◽  
C. A. Papachristodoulou ◽  
K. G. Ioannides ◽  
S. Pavlides
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Optically Stimulated Luminescence ◽  
Osl Dating ◽  
Luminescence Dating ◽  
Alluvial Deposits ◽  
X Ray ◽  
Fault Block ◽  
Optically Stimulated Luminescence Dating ◽  
Dating Method

We have investigated the application of luminescence dating to sediment and pottery samples from a paleoseismological trench excavated in the Gyrtoni Fault, Tyrnavos Basin, Central Greece. The samples were dated following the optically stimulated luminescence (OSL) dating method, using the Riso TL/OSL DA-20 reader. The OSL ages were obtained from chemically purified quartz and a single-aliquot regenerative-dose (SAR) protocol was followed for the equivalent dose (De) determination. Additionally, samples were collected and analyzed with the method of X-ray Fluorescence (XRF) spectrometry, in order to assess their elemental composition. Radioisotope sources (109Cd and 241Am) were used for sample excitation, while X-ray spectra were acquired using a Si(Li) detector coupled with standard electronics. The XRF data were submitted to principal component analysis (PCA). This statistical handling aimed to distinguish from which part of the upthrown fault block scarp-derived colluvium and alluvial deposits, parts of the downthrown block were derived and thus estimate the displacement. The results indicated that both the OSL dating method and the XRF analysis combined with PCA can serve as useful tools for paleoseismological investigations.

scholarly journals The Acquisition Rate and Soundness of a Low-Cost Data Acquisition System (LC-DAQ) for High Frequency Applications

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 524 ◽  
Author(s):  
Ciro Moreno ◽  
Alejandro González ◽  
José Luis Olazagoitia ◽  
Jordi Vinolas
Keyword(s):  
Data Acquisition ◽  
High Frequency ◽  
High Speed ◽  
Low Cost ◽  
Data Retrieval ◽  
Cost Data ◽  
High Rate ◽  
Acquisition Rate ◽  
Open Platform ◽  
Speed Up

This article presents a novel and reliable low-cost data acquisition solution for high frequency and real-time applications in vehicular dynamics. Data acquisition systems for highly dynamic systems based on low-cost platforms face different challenges such as a constrained data retrieval rate. Basic data reading functions in these platforms are inefficient and, when used, they limit electronics acquisition rate capabilities. This paper explains a new low-cost, modular and open platform to read different types of sensors at high speed rates. Conventional reading functions are avoided to speed up acquisition rate, but this negatively affects data reliability of the system. To solve this and exploit higher data managing rates, a number of custom secure layers are implemented to secure a reliable acquisition. This paper describes the new low-cost electronics developed for high rate acquisition applications and inspects its performance and robustness against the introduction of an increasing number of sensors connected to the board. In most cases, acquisition rates of the system are duplicated using this new solution.

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).

Multi-Variate Analysis of Trace Elements from XRF Studies for Classification According to Origin

1992 ◽  
Vol 46 (5) ◽  
pp. 843-847 ◽  
Author(s):  
C. T. Yap
Keyword(s):  
Principal Component Analysis ◽  
Trace Elements ◽  
Geographical Origin ◽  
Principal Component ◽  
Energy Dispersive ◽  
Fluorescence Technique ◽  
X Ray ◽  
Elegant Method ◽  
Variate Analysis ◽  
Made In

The concentrations of twelve trace elements (Mn, Fe, Co, Ni, Cu, Zn, As, Rb, Sr, Y, Zr, and Nb) in 143 pieces of Chinese porcelain made in Jingdezhen, China and elsewhere were obtained with the use of the energy-dispersive x-ray fluorescence technique. An elegant method of multi-variate analysis, known as principal component analysis, was successfully employed in fingerprinting the geographical origin of the porcelain samples.

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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