Simultaneous determination of lead and sulfur by energy-dispersive x-ray spectrometry. Comparison between artificial neural networks and other multivariate calibration methods

Facchin, Ileana; Mello, César; Bueno, Maria Izabel M. S.; Poppi, R J

doi:10.1002/(sici)1097-4539(199905/06)28:3<173::aid-xrs333>3.0.co;2-s

Cited by 15 publications

(3 citation statements)

References 20 publications

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“…Other examples include classification of synthetic and natural polymers using total reflection XRF in combination of principal component analysis (PCA) and cluster analysis . The strongly overlapping sulfur K and Pb M‐series lines in EDXRF spectra have been the subject of a detailed study into the use of chemometric techniques for solving their well‐known spectral complexities …”

Section: Introductionmentioning

confidence: 99%

Principal component analysis‐assisted energy dispersive X‐ray fluorescence spectroscopy for non‐invasive quality assurance characterization of complex matrix materials

et al. 2012

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The analytical challenges in direct quality assurance analysis of complex matrices (extreme matrix effects, spectral overlap, poor signal-to-noise ratio (SNR) for trace analytes, 'dark matrix', imprecise geometry, need for sample integrity) by energy dispersive X-ray fluorescence (EDXRF) spectrometry necessitate development of novel techniques for material characterization. We demonstrate the utility of principal component analysis (PCA) in isotope-excited EDXRF spectrometry of a complex matrix (in this case lubricating oil) in the context of a newly developed EDXRF and scattering (EDXRFS) technique. Lubricating oil quality may be interpreted in terms of its viscosity, anti-wear, anti-oxidation, and anti-rust properties, which are detectable via B, Ca, Mg, Zn, Fe, Na additives (quality markers). Our method involves simultaneous non-invasive acquisition of both fluorescence and scatter spectra from samples held in a propylene dish, and their modeling in a reduced multidimensional space for an interpretable overview that is analytically more useful than, and complementary to, fluorescence peak-based quantitation of the additives; by this method, only Fe and Zn are directly detectable, but with SNR of the fluorescence peak 15-20 times poorer compared with analysis after sample digestion. Although Fe and Zn cannot distinguish the various lubricating oil brands, it can differentiate authentic from adulterated. The method was however found to be analytically useful when combined with PCA: various brands of lubricating oil were discriminated in addition to the detection of adulteration. PCA processing of the spectra showed that the most important quality assurance spectral signature information responsible for the success is contained in the scatter region (low-Z elements). Evaluation of the performance of the method with respect to SNR (i.e. analysis time and therefore speed) showed that there was no significant difference in method performance of analysis live time in the range 100-1000 s, showing proof of concept for rapid characterization of complex matrix materials by PCA-assisted EDXRFS.

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Section: Introductionmentioning

confidence: 99%

Principal component analysis‐assisted energy dispersive X‐ray fluorescence spectroscopy for non‐invasive quality assurance characterization of complex matrix materials

et al. 2012

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“…The regression models were calibrated and validated by cross‐validation method. The predictive ability of the regression models was evaluated by coefficient of multiple determination ( R 2 ) and accuracy determined by standard error of prediction (SEP) Eqn

S E P (%) = {([], \frac{\sum_{i = 1}^{n} {((), C_{ij} - {\hat{C}}_{ij})}^{2}}{p})}^{\frac{true}{1}}

where C ij is the actual concentration,

{\hat{C}}_{ij}

is the estimated concentration of the j th component of the i th sample, c m is the average concentration of the analytes, and p is the number of samples used in the validation set.…”

Section: Methodsmentioning

confidence: 99%

“…The predictive wileyonlinelibrary.com/journal/xrs ability of the regression models was evaluated by coefficient of multiple determination (R 2 ) and accuracy determined by standard error of prediction (SEP) Eqn (2). [32] SEP % ð Þ ¼…”

Section: Calibration By Pls Regressionmentioning

confidence: 99%

Feasibility for chemometric energy dispersive X‐ray fluorescence and scattering (EDXRFS) spectroscopy method for rapid soil quality assessment

et al. 2011

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Soil quality assessment (SQA) is important for modulating agricultural productivity and thus requires simple and rapid analysis of soil (macro & micro) nutrients (here called soil quality indicators – SQIs). We report proof of concept of a chemometrics‐assisted energy dispersive X‐ray fluorescence and scattering (EDXRFS) spectroscopy technique for direct rapid analysis of SQIs. The EDXRFS method exploits, in addition to fluorescence, the X‐ray scatter peaks obtained non‐invasively from soils to develop a calibration strategy for quantitative analysis of SQIs in model clay soils doped with micronutrients (Fe, Cu, and Zn) and macronutrients (NO3−, SO42−, and H2PO4−). The soil samples and certified reference materials IAEA Soil‐7 and IAEA Soil‐1 (used to build spectral library for soil classification) were irradiated at various live times (to simulate different signal‐to‐noise ratios of analyte signals and analysis speed) in a teflon holder and were analyzed using a 109Cd‐excited XRF spectrometer. Principal components analysis was used for spectral data compression and pattern recognition (including for those SQI spectral signatures without any visibly discernible characteristic X‐ray lines), whereas partial least squares regression and artificial neural networks were used to build a calibration and quantitative analysis strategy. The method furnishes soil micronutrient and macronutrient information simultaneously and rapidly (t = 200 s). To the best of the authors' knowledge, this is the first time that an XRF method has demonstrated spectroanalytical potential for quantitative macronutrients analysis in soils applicable to routine SQA. Coupling EDXRFS with multivariate chemometrics enables rapid and reliable assessment of chemical SQIs. Copyright © 2011 John Wiley & Sons, Ltd.

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Determination of some rare earth elements by EDXRF and artificial neural networks

Schimidt¹,

Cornejo-Ponce²,

Bueno³

et al. 2003

X-Ray Spectrometry

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This paper describes the simultaneous determination of Pr, Nd and Sm by EDXRF spectrometry using mixtures of oxides of these metals in a silica matrix. The data were treated by distinct neural network algorithms: back-propagation (BP), Levenberg-Marquardt (LM) and two variations of back-propagation (called BP-SC, single component, and BP-MC, multiple component), using results from the PLS model (partial least square regression) for comparison. The best applied model was the BP-SC neural network, which produced relative standard errors of prediction of 17.5% for Pr, 12.5% for Nd and 12.6% for Sm.

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Simultaneous determination of lead and sulfur by energy-dispersive x-ray spectrometry. Comparison between artificial neural networks and other multivariate calibration methods

Cited by 15 publications

References 20 publications

Principal component analysis‐assisted energy dispersive X‐ray fluorescence spectroscopy for non‐invasive quality assurance characterization of complex matrix materials

Principal component analysis‐assisted energy dispersive X‐ray fluorescence spectroscopy for non‐invasive quality assurance characterization of complex matrix materials

Feasibility for chemometric energy dispersive X‐ray fluorescence and scattering (EDXRFS) spectroscopy method for rapid soil quality assessment

Determination of some rare earth elements by EDXRF and artificial neural networks

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