Energy dispersive X-ray fluorescence spectrometry characterization of wall mortars with principal component analysis: Phasing and ex situ versus in situ sampling

2020 ◽  
Vol 43 ◽  
pp. 90-97
Author(s):  
Mary Kate Donais ◽  
Mina Alrais ◽  
Kaliopi Konomi ◽  
David George ◽  
William H. Ramundt ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Energy Dispersive ◽  
Ex Situ ◽  
Fluorescence Spectrometry ◽  
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 Characterization of insulin microcrystals using powder diffraction and multivariate data analysis

2006 ◽  
Vol 39 (3) ◽  
pp. 391-400 ◽  
Author(s):  
Mathias Norrman ◽  
Kenny Ståhl ◽  
Gerd Schluckebier ◽  
Salam Al-Karadaghi
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Data Analysis ◽  
Powder Diffraction ◽  
Multivariate Data Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray ◽  
Structural Differences

Twelve different microcrystalline insulin formulations were investigated by X-ray powder diffraction and were shown to have very characteristic patterns. Three of the formulations crystallize in the same crystal system, but have structural differences in the N-terminal B-chain of the insulin molecule. This difference was efficiently detected in the powder patterns. The sensitivity of the method makes it a valuable tool for characterization of microcrystalline samples. By use of principal-component analysis, the twelve different formulations originating from six different crystal systems were classified into nine separate clusters. The powder patterns of each cluster can now be used as `fingerprints' for the different insulin polymorphs. The combination of X-ray powder diffraction and multivariate analysis, such as principal-component analysis, provides a rapid and effective tool for studying the influence of derivatives, additives, ions, pHetc., in the crystallization media.

Applications of principal component analysis to pair distribution function data

2015 ◽  
Vol 48 (6) ◽  
pp. 1619-1626 ◽  
Author(s):  
Karena W. Chapman ◽  
Saul H. Lapidus ◽  
Peter J. Chupas
Keyword(s):  
Principal Component Analysis ◽  
Distribution Function ◽  
Pair Distribution Function ◽  
Principal Component ◽  
Component Analysis ◽  
Scattering Data ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Developments in X-ray scattering instruments have led to unprecedented access toin situand parametric X-ray scattering data. Deriving scientific insights and understanding from these large volumes of data has become a rate-limiting step. While formerly a data-limited technique, pair distribution function (PDF) measurement capacity has expanded to the point that the method is rarely limited by access to quantitative data or material characteristics – analysis and interpretation of the data can be a more severe impediment. This paper shows that multivariate analyses offer a broadly applicable and efficient approach to help analyse series of PDF data from high-throughput andin situexperiments. Specifically, principal component analysis is used to separate features from atom–atom pairs that are correlated – changing concentration and/or distance in concert – allowing evaluation of how they vary with material composition, reaction state or environmental variable. Without requiring prior knowledge of the material structure, this can allow the PDF from constituents of a material to be isolated and its structure more readily identified and modelled; it allows one to evaluate reactions or transitions to quantify variations in species concentration and identify intermediate species; and it allows one to identify the length scale and mechanism relevant to structural transformations.

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