Combining Selective Sequential Extractions, X-ray Absorption Spectroscopy, and Principal Component Analysis for Quantitative Zinc Speciation in Soil

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.

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Combining selective sequential extractions, X-Ray Absorption Spectroscopy, and X-Ray Powder Diffraction for Cu (II) speciation in soil and mineral phases

EURASIAN JOURNAL OF SOIL SCIENCE (EJSS) ◽

10.18393/ejss.286544 ◽

2017 ◽

Vol 6 (2) ◽

pp. 114-114 ◽

Cited By ~ 1

Author(s):

Tatiana Minkina ◽

Dina Nevidomskaya ◽

Alexander Soldatov ◽

David Pinskii ◽

Fariz Mikailsoy ◽

...

Keyword(s):

Absorption Spectroscopy ◽

Powder Diffraction ◽

Sequential Extractions ◽

X Ray ◽

Mineral Phases ◽

X Ray Absorption

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Plastic Classification with X-ray Absorption Spectroscopy Based on Back Propagation Neural Network

Applied Spectroscopy ◽

10.1177/0003702817706921 ◽

2017 ◽

Vol 71 (11) ◽

pp. 2538-2548 ◽

Cited By ~ 4

Author(s):

Qian Wang ◽

Xiaomei Wu ◽

Lingcong Chen ◽

Zheng Yang ◽

Zheng Fang

Keyword(s):

Absorption Spectroscopy ◽

Recognition Rate ◽

Back Propagation ◽

Principal Component ◽

Back Propagation Neural Network ◽

X Ray ◽

Analysis Methods ◽

Average Recognition Rate ◽

The Stability ◽

X Ray Absorption

Currently, spectral analysis methods used in the classification of plastics have limitations that do not apply to opaque plastics or the stability of experimental results is not strong. In this paper, X-ray absorption spectroscopy (XAS) has been applied to classify plastics due to its strong penetrability and stability. Fifteen kinds of plastics are selected as specimens. X-ray, which is excited by a voltage of 60 kV, penetrated these specimens. The spectral data acquired by CdTe X-ray detector are processed by principal component analysis (PCA) and other data analysis methods. Then the back propagation neural networks (BPNN) algorithm is used to classify the processed data. The average recognition rate reached 96.95% and classification results of all types of plastic results were analyzed in detail. It indicates that XAS has the potential to classify plastics and that XAS can be used in some fields such as plastic waste sorting and recycling. At the same time, the technology of XAS, in the future, can also be used to classify more substances.

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Variation of elemental concentration in leather during post-tanning operation using energy dispersive X-ray fluorescence spectroscopy: principal component analysis approach

International Journal of Environmental & Analytical Chemistry ◽

10.1080/03067319.2020.1746292 ◽

2020 ◽

pp. 1-13

Author(s):

Kallen Mulilo Nalyanya ◽

Ronald K. Rop ◽

Arthur S. Onyuka ◽

Zephania Birech ◽

Justus J. Okonda

Keyword(s):

Principal Component Analysis ◽

Fluorescence Spectroscopy ◽

Elemental Concentration ◽

Principal Component ◽

Component Analysis ◽

Energy Dispersive ◽

Analysis Approach ◽

X Ray

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Principal Component Analysis of Multiwavelength Properties of Seyfert Galaxies

Symposium - International Astronomical Union ◽

10.1017/s0074180900176752 ◽

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).

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