Quantitative energy-dispersive x-ray fluorescence analysis of liquids using partial least-squares regression

Lemberge, P.; Espen, Pierre J. Van

doi:10.1002/(sici)1097-4539(199903/04)28:2<77::aid-xrs312>3.0.co;2-c

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…Energy‐dispersive X‐ray fluorescence spectrometry is a versatile method capable of elemental analysis of various materials including solids (Johnson et al, ), powders (Papachristodoulou et al .,) and liquids (Lemberge and Van Espen ), with recent work in this group using this technology for the characterisation of dairy processing sludge (Daly et al …”

Section: Introductionmentioning

confidence: 99%

Energy‐dispersive X‐ray fluorescence spectrometry as a tool for the rapid determination of the five major minerals (Na, Mg, K, P and Ca) in skim milk powder

McCarthy

Daly

Fenelon

et al. 2019

Int J of Dairy Tech

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Milk and dairy products are comprised of a complex matrix of components that can interfere with analysis and, as a result, are often not suitable for direct introduction into analytical instruments. Energy‐dispersive X‐ray fluorescence spectrometry (EDXRF) can bypass time‐consuming preparation steps, with potential for rapid, onsite analysis of minerals. In this study, five major milk minerals, sodium, magnesium, potassium, phosphorus and calcium, were quantified in dairy powders, using EDXRF, and the systemic bias of the method was evaluated by Bland–Altman analysis. No significant systemic bias was observed for the quantification of sodium, magnesium, potassium and phosphorus, meaning that EDXRF has potential to be used as a rapid offline analytical technology suitable for the analysis of these minerals in skim milk powders.

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Section: Introductionmentioning

confidence: 99%

Energy‐dispersive X‐ray fluorescence spectrometry as a tool for the rapid determination of the five major minerals (Na, Mg, K, P and Ca) in skim milk powder

McCarthy

Daly

Fenelon

et al. 2019

Int J of Dairy Tech

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“…They were also mean centered before building the PLS model. It has the effect that every channel has the same importance (weight) in the analysis …”

Section: Methodsmentioning

confidence: 99%

“…Partial least squares regression‐based methods have been proven to be a good alternative for quantification in XRF spectroscopy . These methods are fast and easy to use though giving satisfactory results under some conditions.…”

Section: Introductionmentioning

confidence: 99%

Improvement of PLS regression‐based XRF spectroscopy quantification using multiple step procedure and Monte Carlo simulation

et al. 2013

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Partial least squares (PLS) regression-based methods have been proven to be a good alternative for quantification in X-ray fluorescence spectroscopy. These methods are fast and easy to use though giving satisfactory results under certain conditions. One of these conditions is the necessity of having a great number of spectra to build the model (training set). The choice of the constituent concentration range in the training set has a big influence on the accuracy of the model. Better accuracy is obtained if the model is built in relatively narrow regions containing (or close to) the real concentration value.In the present work, Monte Carlo (MC) simulated spectra are used to form the training set. The advantage to use MC generated training spectra is the unlimited availability of perfect standards.This paper aims to improve the accuracy of the method by introducing a multiple step procedure in order to build the PLS model using narrow concentration range close to (or containing) the real concentration values in the samples to be measured.This approach consists of an initial guess of the constituents' concentrations and a preliminary PLS model before building the final model. The prediction of ten MC simulated alloy standard samples containing Ti, Mn, Fe, Co, Cu, Zn, Sr, Zr, and Mo using this method allowed to have average relative prediction errors less than 5% for elements with narrow concentration ranges.

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“…Although newer quantitative methods of analysis try to directly use the original XRF spectral data, [10][11][12][13] quantitative XRF analysis typically starts with a spectral evaluation of which the results (net peak intensities) are used to interpret qualitatively the elemental distributions (e.g. images) prior to the eventual conversion of the elemental intensity values into elemental concentrations.…”

Section: Spectum Evaluationmentioning

confidence: 99%

Processing of three-dimensional microscopic X-ray fluorescence data

Vekemans

Vincze

Brenker

et al. 2004

J. Anal. At. Spectrom.

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Quantitative energy-dispersive x-ray fluorescence analysis of liquids using partial least-squares regression

Cited by 16 publications

References 14 publications

Energy‐dispersive X‐ray fluorescence spectrometry as a tool for the rapid determination of the five major minerals (Na, Mg, K, P and Ca) in skim milk powder

Energy‐dispersive X‐ray fluorescence spectrometry as a tool for the rapid determination of the five major minerals (Na, Mg, K, P and Ca) in skim milk powder

Improvement of PLS regression‐based XRF spectroscopy quantification using multiple step procedure and Monte Carlo simulation

Processing of three-dimensional microscopic X-ray fluorescence data

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