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.

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.

Simultaneous determination of trace elements in lavage fluids from human bronchial alveoli by energy-dispersive x-ray fluorescence. 3. Routine analysis.

1987 ◽  
Vol 33 (12) ◽  
pp. 2234-2239 ◽  
Author(s):  
E A Maier ◽  
A Dietemann-Molard ◽  
F Rastegar ◽  
R Heimburger ◽  
C Ruch ◽  
...  
Keyword(s):  
Trace Elements ◽  
Occupational Exposure ◽  
Bronchoalveolar Lavage ◽  
Normal Values ◽  
Energy Dispersive ◽  
Fluorescence Technique ◽  
X Ray ◽  
High Concentrations ◽  
Number Of Cells

Abstract We applied the energy-dispersive x-ray fluorescence technique to determination of trace elements in human bronchoalveolar lavage fluids. Our analysis of more than 200 samples allowed us to determine normal reference values, to be used in characterizing occupational exposure. These values are expressed both in nanograms per 1000 cells (of all kinds) and nanograms per 1000 macrophages to correlate lavage efficiency and dust content of the alveoli. The result expressed in milligrams per liter is not sufficient, because some healthy volunteers showed high concentrations of iron but normal values when expressed vs the number of cells. Some examples of abnormal compositions of broncho-alveolar lavages are reported and the fully automated spectrometer developed for clinical and biological investigations is described.

scholarly journals Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin

2019 ◽  
Vol 412 (2) ◽  
pp. 463-472 ◽  
Author(s):  
Yiannis Fiamegos ◽  
Catalina Dumitrascu ◽  
Michele Ghidotti ◽  
Maria Beatriz de la Calle Guntiñas
Keyword(s):  
Geographical Origin ◽  
External Validation ◽  
Principal Component ◽  
Energy Dispersive ◽  
Sample Treatment ◽  
Classification Models ◽  
Botanical Variety ◽  
X Ray ◽  
Food Commodities

AbstractHoney is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of certain macro- and trace elements, determined by energy-dispersive X-ray fluorescence (ED-XRF) without any type of sample treatment, were used to classify honeys according to botanical variety and geographical origin. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to create classification models for nine different botanical varieties—orange, robinia, lavender, rosemary, thyme, lime, chestnut, eucalyptus and manuka—and seven different geographical origins—Italy, Romania, Spain, Portugal, France, Hungary and New Zealand. Although characterised by 100% sensitivity, PCA models lacked specificity. The PLS-DA models constructed for specific combinations of botanical variety-country (BV-C) allowed the successful classification of honey samples, which was verified by external validation samples.

PC/MAC* Image Processing Freeware for Examining Spectral Images

2000 ◽  
Vol 6 (S2) ◽  
pp. 1056-1057
Author(s):  
D. S. Bright
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Low Voltage ◽  
Processing System ◽  
Principal Component ◽  
Public Domain ◽  
Data Cube ◽  
Energy Dispersive ◽  
X Ray ◽  
Adjacent Channels

MacLispix, a public domain image processing system for the Macintosh*, has been applied to a variety of image processing problems, such as using Principal Component Analysis to explore correlated images. The tools provided by MacLispix are now available in Lispix, the updated version that runs on both the PC and the Macintosh.I will illustrate the utility of Lispix by way of an example ‘data cube', a low voltage energy dispersive x-ray spectrum image provided by Ian Anderson. The ‘cube'is 200x150 pixels, each pixel having a spectrum of 512 two-byte channels The spectra were smoothed and reduced by adding adjacent channels to reduce them to 256 channels each. Since Lispix is image oriented rather than spectrum oriented, the reduced cube is stored, and represented internally as 256 images (one image for each channel in the spectrum), rather than as 200x150 spectra (one spectrum for each pixel in the image).

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