Quantitative Analysis with XRF: Mining Applications

Webinar summary

In this webinar, Carmen discusses how X-ray fluorescence (XRF) is widely used for elemental analysis in mining and geochemical applications, supporting activities ranging from exploration and grade control to mineral processing and quality assurance.

Different types of XRF instrumentation serve different analytical needs. Handheld and energy dispersive XRF (EDXRF) systems are useful for rapid screening and field analysis, while wavelength dispersive XRF (WDXRF) provides higher resolution, sensitivity, precision, and accuracy for laboratory analysis. XRF complements techniques such as X-ray diffraction (XRD), which provides information about mineral structure.

Pressed powder preparation is commonly used for mining samples because it provides a relatively simple approach to routine analysis. However, analytical performance depends strongly on sample preparation. Moisture, particle size, sample homogeneity, pellet density and thickness, surface condition, and contamination from grinding equipment can all affect measured X-ray intensities. Samples should be dried appropriately, crushed, pulverized, split to obtain a representative portion, and finely milled. A typical target is for at least 95% of the final material to pass a 45 µm sieve. The powder is then mixed with a binder and pressed into a mechanically stable pellet with a flat, smooth analytical surface.

Accurate quantitative analysis also requires suitable calibration standards covering the appropriate concentration range, with standards prepared in the same manner as unknown samples. Mining samples present significant matrix effects because multiple elements coexist within complex mineral matrices. Absorption and enhancement effects can distort the relationship between X-ray intensity and concentration, requiring mathematical corrections. These can include theoretical alpha corrections, internal-standard ratio methods, and corrections based on Compton scattering.

An internal-standard approach can compensate for matrix effects by adding a constant amount of another element and ratioing its intensity against that of the analyte. For iron ore, for example, cobalt can serve as an internal standard, with the ratio of Fe Kβ to Co Kα used for correction. Monitoring the cobalt intensity can also provide a check on the consistency of the grinding process.

Compton scattering provides another ratio-based approach. Quant Scatter FP combines scattering information with theoretical fundamental-parameter corrections to compensate for matrix effects, including particle-size effects that theoretical alpha corrections alone cannot address. Examples involving iron ore, copper ore and concentrates, and nickel sulfide ore and concentrates demonstrate improved calibration fits and agreement with certified reference values when these correction methods are combined. The scattering approach is primarily applicable to heavier elements, generally from titanium onward, rather than lighter elements such as silicon.

Fundamental parameters (FP) calibration can also address materials with substantially different matrices. Unlike empirical calibration, which generally requires matrix-matched standards, FP calibration uses calculated theoretical intensities together with measured intensities. An application involving different marl compositions showed that multiple matrices could be incorporated into a single calibration and that pressed-powder results were generally close to reference results obtained using fused beads. A limitation is that the elemental composition must be sufficiently well characterized to account for essentially 100% of the sample.

Reliable XRF analysis ultimately depends on the entire analytical process rather than the spectrometer alone. Robust and consistent sample preparation, sufficient calibration standards, appropriate analytical ranges, effective matrix corrections, method validation, drift correction, and continuing assessment are all important. Proficiency-testing programs can provide an additional means of benchmarking laboratory performance and identifying opportunities to improve analytical quality.