Assessment of the Effect of Soil Sample Preparation, Water Content and Excitation Time on Proximal X-ray Fluorescence Sensing

Abstract

X-ray fluorescence (XRF) spectroscopy offers a fast and efficient method for analysing soil elemental composition, both in the laboratory and the field. However, the technique is sensitive to spectral interference as well as physical and chemical matrix effects, which can reduce the precision of the measurements. We systematically assessed the XRF technique under different sample preparations, water contents, and excitation times. Four different soil samples were used as blocks in a three-way factorial experiment, with three sample preparations (natural aggregates, ground to ≤2 mm and ≤1 mm), three gravimetric water contents (air-dry, 10% and 20%), and three excitation times (15, 30 and 60 s). The XRF spectra were recorded and gave 540 spectra in all. Elemental peaks for Si, K, Ca, Ti, Fe and Cu were identified for analysis. We used analysis of variance (anova) with post hoc tests to identify significant differences between our factors and used the intensity and area of the elemental peaks as the response. Our results indicate that all of these factors significantly affect the XRF spectrum, but longer excitation times appear to be more defined. In most cases, no significant difference was found between air-dry and 10% water content. Moisture has no apparent effect on coarse samples unless ground to 1 mm. We suggested that the XRF measurements that take 60 s from dry samples or only slightly moist ones might be an optimum option under field conditions.

Optimization of sample preparation and data evaluation techniques for X‐ray fluorescence prediction of soil texture, pH, and cation exchange capacity of loess soils

“…These positive findings encourage exploration of further efficiency gains to the modeling approaches demonstrated here through the use of standard reference materials rather than site-specific corrections to XRF elemental contents, as well as through measurement of intact, field-moist soils, as in the recent study by Li et al (2022). For this, empirical corrections for soil moisture have proven helpful (Stockmann, Jang et al, 2016), but effects of poor detection of light elements (Na, Mg, Al, Si, P, and S)-which were highly important predictors in the current study-must be considered.…”

“…The explanation for the inconsistent to minor improvements from measurement of press pellets could be that sufficient steps were already taken to ensure the homogeneity of powder samples. These steps included drying and grinding (Goff et al., 2020; Li et al., 2022), rotation of the sample during measurement to compensate for minor sample inhomogeneities and surface roughness, and measurement in He atmosphere with reduced air pressure to limit fluorescence attenuation, which could vary for uncompacted powder samples (Haschke et al., 2021).…”

“…For example, infrared spectroscopy studies have demonstrated high model robustness in independent validation for spectrally active soil properties (e.g., clay), but that local calibration is often essential for spectrally inactive properties (e.g., pH and CEC) (Soriano‐Disla et al., 2014). While a large number of XRF studies predicting soil properties have been conducted, typically only one model calibration/validation strategy has been applied in each study (e.g., Javadi & Mouazen, 2021; Kandpal et al., 2022; Li et al., 2022; O'Rourke, Minasny et al., 2016, O'Rourke, Stockmann et al., 2016; Sharma et al., 2015; Tavares et al., 2020; Zhu et al., 2011). Thus, a mechanistic investigation of the robustness of XRF predictions for various soil properties with and without independent validation is required.…”

“…Further optimizations of XRF application are also worth exploring, including the benefits of various approaches to sample preparation and data evaluation. The degree of sample preparation affects the trade‐off between the accuracy and efficiency of soil characterization with XRF (Li et al., 2022). While field measurement of intact soil samples saves time and expense (Goff et al., 2020), enabling a much higher spatiotemporal resolution of data to be collected, soil moisture content absorbs X‐rays and scatters primary radiation, resulting in poor detection of light elements (Haschke et al., 2021) and thus lower model accuracy.…”