F13 Fundamental Parameter Method Using Scattering X-rays in X-ray Fluorescence Analysis

Kataoka, Yuki; Kawahara, Naoki; Hara, Shinya; Yamada, Yasujiro; Matsuo, Takashi; Mantler, Michael

doi:10.1154/1.1968348

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The secondary targets used for this study were Mo, Cu, Rx9, and Si. All samples for the sample preparation study were analysed with the FP [24,32,33]. Operationally the Rigaku EDXRF system allows the creation of 'alpha corrections' or matching libraries to correct FP-derived analytical results.…”

Section: Edxrf Analysismentioning

confidence: 99%

Optimising Sample Preparation and Calibrations in EDXRF for Quantitative Soil Analysis

et al. 2020

View full text Add to dashboard Cite

Energy-dispersive X-ray fluorescence spectrometry (EDXRF) is a rapid and inexpensive method for soil analysis; however, analytical results are influenced by particle size effects and spectral interferences. The objective of this study was to optimise sample preparation and calibrations to improve the accuracy of EDXRF for soil tests. Methods of sample preparation were compared by calculating the recoveries of 13 elements in four International Soil-Analytical Exchange (ISE) standards prepared as loose powder (LP), pressed pellet (PP), and pressed pellet with wax binder (PPB). A matching library (ML) was created and evaluated against the fundamental parameters (FP) calibration using 20 ISE standards. Additionally, EDXRF results of 41 tillage soils were compared with Inductively coupled plasma optical emission spectrometry (ICP-OES) results. The PPB had most recoveries within the acceptable range of 80–120%; conversely, PP yielded the poorest element recoveries. For the calibration, the ML provided better recoveries of Ni, Pb, Cu, Mg, S, P, and Cr; however, for Zn, and Mn, it had the opposite effect. Furthermore, EDXRF results compared with ICP-OES separated by soil texture class for Al, K, Mn, and Fe. In conclusion, the EDXRF is suitable for quantifying both trace elements and macronutrients in contaminated soils and has the potential to provide screening or prediction of soil texture in agriculture.

show abstract

Section: Edxrf Analysismentioning

confidence: 99%

Optimising Sample Preparation and Calibrations in EDXRF for Quantitative Soil Analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The Rigaku fundamental parameters software assumes that absorbance and enhancement effects can affect concentrations and provides a coefficient to correct for these matrix effects. 21,22 This function improved the correlation coefficients for all elements by correcting for the effects of K, Co, Ca, P, and Mo (Table III). Concentrations determined using empirical calibration are denoted by the term XRF_emp.…”

Section: Resultsmentioning

confidence: 99%

Application of Energy Dispersive X-ray Fluorescence Spectrometry to the Determination of Copper, Manganese, Zinc, and Sulfur in Grass (Lolium perenne) in Grazed Agricultural Systems

Daly

Fenelon

2018

Appl Spectrosc

View full text Add to dashboard Cite

Conventional methods for the determination of major nutrients and trace elements in grass rely on acid digestion followed by analysis using inductively coupled plasma optical emission spectrometry (ICP-OES), which can be both time consuming and costly. Energy dispersive X-ray fluorescence (EDXRF) spectrometry offers a rapid alternative that can determine multiple elements in a single scan. Copper, Mn, Zn, and S in grass samples were determined using EDXRF with a number of different calibration approaches using both empirical standards and the theoretical relationships between concentrations and intensities. Using an existing archive of 467 grass samples of known concentrations, a suite of 30 samples was selected as empirical grass standards to build a calibration set between sample concentrations and EDXRF intensities. The theoretical or standardless approach used the fundamental parameters method to determine element concentrations. To validate the two calibration methods, 59 samples were randomly selected from the same archive and database and analyzed by EDXRF. The measurements of Cu, Mn, Zn, and S were compared with the ICP-OES values using agreement statistics. An excellent correlation was observed between the concentrations determined by EDXRF and ICP-OES ( R > 0.90) regardless of the calibration approach. However, agreement and closeness to the true value varied and were assessed using agreement statistics. Across all elements, the empirically calibrated samples were in excellent agreement with the values determined by ICP-OES. The theoretical calibrations provided excellent agreement for Mn and Zn, but a degree of fixed and proportional bias was observed in the Cu and S values. Fixed bias was corrected by subtracting the computed bias from the EDXRF concentrations and improved the overall agreement. Similarly, proportional bias was corrected using the linear regression model to predict the corrected EDXRF values. This improved the overall agreement with the ICP-OES values for both Cu and S using corrected fundamental parameters calibrations. This study provides a practical basis for the use of EDXRF to determine Cu, Mn, Zn, and S in grass samples to monitor forage quality in grazed systems without the need for sample digestion. The observed fixed and proportional bias in the theoretical calibrations can be corrected provided that a good correlation exists between EDXRF and conventional methods.

show abstract

“…The study's secondary targets were Mo, Cu, Rx9, and Si. The fewer fundamental parameters technique (FP), a standard procedure, was used to analyze the samples (Omote et al, 1995;Kataoka et al, 2006). The Rigaku EDXRF system's operational features allow the development of "alpha corrections" or matching libraries to rectify FP-derived analytical data.…”

Section: Preparation and Laboratory Analysis Of Samplesmentioning

confidence: 99%

Quantitative determination of heavy metals around schools and automobile workshops near frequented roads in Kaduna State, Nigeria

Aliyu M.,

Oladipo, M.O.A,

Adeyemo, D.J.

et al. 2022

USci

View full text Add to dashboard Cite

Heavy metals are metallic elements with high density compared to other metals and are toxic even at minute concentrations. The concentration of these metals around schools and automobile workshops near frequented roads in Kaduna State, Nigeria, was reported in the present work. Soil samples were collected at varying distances from the major roads (0 – 10m) and at different depths (0 – 60cm). The elemental composition of the samples was determined using Next-Generation Cartesian Geometry Energy Dispersive X-Ray Fluorescence Spectrometer (NEX CG EDXRF) and the statistical correlation and clustering analysis were carried out using SPSS and Mini tab, respectively. The heavy metals Sn, Hf, Ta, V, Cr, Zn, Ir, Pt, Au, Mn, Fe, Co, Ni, Cu, Pb, Th, Ga, As, U, and Zr were considered in the analysis, and their concentrations were presented as Average ± standard deviation. Pearson correlation coefficient indicated different levels of relationships among the heavy metals, while Clustering analysis grouped the soil samples into three major clusters, further confirming the interrelationship among the samples. The concentrations of As, Pb, Cd, Co, Ni were found to be of serious health concern, considering that in almost all the zones in Kaduna state, their average concentrations were greater than the threshold provided by WHO/FAO. Findings from this study indicated that it is unsafe for children to play near schools /auto-mechanic workshops located along highways.

show abstract

F13 Fundamental Parameter Method Using Scattering X-rays in X-ray Fluorescence Analysis

Cited by 4 publications

References 0 publications

Optimising Sample Preparation and Calibrations in EDXRF for Quantitative Soil Analysis

Optimising Sample Preparation and Calibrations in EDXRF for Quantitative Soil Analysis

Application of Energy Dispersive X-ray Fluorescence Spectrometry to the Determination of Copper, Manganese, Zinc, and Sulfur in Grass (Lolium perenne) in Grazed Agricultural Systems

Quantitative determination of heavy metals around schools and automobile workshops near frequented roads in Kaduna State, Nigeria

Contact Info

Product

Resources

About