Rapid in situ analysis with portable XRF sensor: A pioneering study proposing fitness-for-purpose approach for performance assessment in tropical soils

Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data.

Estimating plant-available nutrients with XRF sensors: Towards a versatile analysis tool for soil condition assessment

Compost is a valuable organic amendment which affords substantive fertility to soils where applied. A common component of compost fertility is cation exchange capacity (CEC), which has traditionally been determined via standard wet chemistry laboratory methods. This research utilized portable X-ray fluorescence (PXRF) spectrometry to evaluate 74 compost samples from the USA and Canada. PXRF elemental data were used for predicting compost CEC via random forest (RF) regression. Comparison between laboratory-determined vs. PXRF predicted CEC produced the following relationships: R2=0.90, RMSE = 5.41 meq 100 g−1 (model calibration) and R2=0.60, RMSE = 8.07 meq 100 g−1 (model validation). A key advantage of this technique is that the same data used for CEC prediction can also yield insight into other compost parameters of interest such as heavy metal content, plant essential nutrient content, salinity, and pH. Taken collectively, the PXRF approach can provide rapid, on-site analysis of compost which was previously not feasible with conventional methods. Our initial study has established the viability of PXRF for compost CEC determination, with further development on a wider array of feedstocks suggested for future study.

Characterizing soils via portable X-ray fluorescence spectrometer: 4. Cation exchange capacity (CEC)

Background and Objective: Pb is a well-known toxic metal that can accumulate in bones over time and still threatening large populations nowadays, even those who are environmentally exposed to it. Strontium (Sr) is a metal directly related to bone health and has been used in the treatment of osteoporosis disease as a supplement. Manganese (Mn) is an essential nutrient in the body, yet excessive Mn is toxic and affecting many organ systems. Another toxic metal, mercury (Hg), has been poising different populations primarily through seafood consumptions, especially inducing neurological disorders in infants and fetuses. Even though significant associations between the above metal exposures and health outcomes have been recognized over the decades, the current technologies are limited in assessing cumulative long-term exposures in vivo to evaluate such associations further. Bone and toenail are appropriate biomarkers to reflect long-term exposure due to the longer half-life of these metals in them than in the traditional biomarkers. Therefore, this work evaluated the usefulness of portable x-ray fluorescence (XRF) technology on in vivo quantification of Pb and Sr in bone, and Mn and Hg in toenail. Materials and Methods: The portable XRF device was calibrated by using the Pb- and Sr-doped bone-equivalent phantoms, and Mn- and Hg-doped nail-equivalent phantoms, correspondingly in different projects. Seventy-six adults (38-95 years of age, 63 ± 11 years) from Indiana, USA, were recruited to participate in this study. For the in vivo bone measurements, each participant was measured at the mid-tibia bone using the portable XRF and K-shell XRF system (KXRF). We estimated the correlation between the bone Pb concentration measured by both devices to evaluate the use of the portable XRF in the bones. Using the portable XRF, the bone Sr exposure of the study population were simultaneously assessed with the bone Pb exposures. Besides, we analyzed the mid-tibia bone Sr data of a Chinese population, which were measured with the same portable XRF device by our research group. We also examined the extent to which the detection limit (DL) of the portable XRF was influenced by scan time and overlying soft tissue thickness for both Pb and Sr. For the exposure assessment of Mn and Hg in toenails, we first established system calibrations and determined the DL with phantoms. In order to validate the portable XRF in a population study, the recruited participants were measured at the big toenail by the device, and their toenail clippings were analyzed by the inductively coupled plasma spectrometry (ICP-MS). Besides, we analyzed the toenail data of an occupationally-exposed population, collected by our collaborators in Boston. A portable XRF device with the same model as ours was used in that study. Results: The uncertainty of in vivo individual bone measurement increased with higher soft tissue thickness overlying bone, and reduced with extending measurement time. With thickness ranging from 2 to 6 mm, the uncertainty of a 3-minute in vivo measurement ranged from 1.8 to 6.3 ug/g (ppm) for bone Pb and from 1.3 to 2.3 ppm for bone Sr. Bone Pb measurements via portable XRF and KXRF were highly correlated: R=0.48 for all participants, and R=0.73 among participants with soft tissue thickness < 6 mm (72% of the sample). A trend of different bone Sr concentrations was observed across the races and sexes. The DL of the portable XRF with 3-minute toenail measurements was 3.59 ppm for Mn and 0.58 ppm for Hg. The portable XRF and ICP-MS measurements were highly correlated in the occupational populations for both Mn (R = 0.59) and Hg (R = 0.75). A positive correlation (R = 0.34) was found for toenail Mn measurements in the environmentally-exposed population, while a non-significant correlation was observed for toenail Hg due to the extremely low-level of Hg (Mean = 0.1 ppm) in the study population. Discussion and Conclusion: The portable XRF could be a valuable tool for non-invasive in vivo quantification of bone Pb and Sr, especially for people with thinner soft tissue; and of toenail Mn and Hg, especially for people with moderate- to high-level exposures.

Measures of soil electrical conductivity (EC) and elevation are relatively inexpensive to collect and result in dense data sets which allow for mapping with limited interpolation. Conversely, soil fertility information is expensive to collect so that relatively few samples are taken and mapping requires extensive interpolation with large estimation errors, resulting in limited usefulness for site-specific applications in precision agriculture. Principal component (PC) analysis and cokriging can be applied to create meaningful field scale summaries of groups of attributes and to decrease the estimation error of maps of the summarized attributes. Deep (0–90 cm) and shallow (0–30 cm) EC, elevation, and soil fertility attributes were measured in fields under corn (Zea mays L.) and soybean (Glycine max L.) rotations, at two sites in Illinois (IL) and two sites in Missouri (MO). Soil fertility and topography attributes were summarized by PC analysis. The first topography PC (TopoPC1) contrasted flow accumulation against elevation and curvature, to describe the main topographic pattern of the fields. The first soil fertility PC (SoilPC1) consistently grouped together cation exchange capacity (CEC), Ca, Mg, and organic matter (OM). SoilPC1 was well correlated to soil EC for all sites and cokriging with EC had higher r 2 in the crossvariogram models compared to ordinary kriging. The second and third soil fertility PCs (SoilPC2 and SoilPC3) were concerned with soil pH and P, and reflected historic land use patterns. Maps of SoilPC2 and SoilPC3 had little relationship to soil EC or topography and so could not be improved by cokriging. Abbreviations: CEC – cation exchange capacity; EC – soil bulk electrical conductivity; OM – soil organic matter; PC – principal component; SoilPC – soil fertility principal component; Sph – spherical variogram function; TopoPC – topography principal component.

Determination of base saturation percentage in agricultural soils via portable X-ray fluorescence spectrometer

In situ determination of soybean leaves nutritional status by portable X-ray fluorescence: An initial approach for data collection and predictive modelling

Assessment of X-ray fluorescence capabilities for nail and hair matrices through zinc measurement in keratin reference materials

The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple linear regressions (MLR) and partial least squares regression (PLSR)) to overcome the soil matrix effect, and subsequently improve the prediction accuracy of key soil fertility attributes. A portable XRF was used for analyzing 102 soil samples collected from two agricultural fields with contrasting soil matrices. Using the intensity of emission lines as input, preprocessing methods included with and without the CN. Univariate regression models for the prediction of clay, cation exchange capacity (CEC), and exchangeable (ex-) K and Ca were compared with the corresponding MLR models to assess matrix effect mitigation. The MLR and PLSR models improved the prediction results of the univariate models for both preprocessing methods, proving to be promising strategies for mitigating the matrix effect. In turn, the CN also mitigated part of the matrix effect for ex-K, ex-Ca, and CEC predictions, by improving the predictive performance of these elements when used in univariate and multivariate models. The CN has not improved the prediction accuracy of clay. The prediction performances obtained using MLR and PLSR were comparable for all evaluated attributes. The combined use of CN with multivariate regressions (MLR or PLSR) achieved excellent prediction results for CEC (R2 = 0.87), ex-K (R2 ≥ 0.94), and ex-Ca (R2 ≥ 0.96), whereas clay predictions were comparable with and without CN (0.89 ≤ R2 ≤ 0.92). We suggest using multivariate regressions (MLR or PLSR) combined with the CN to remove the soil matrix effects and consequently result in optimal prediction results of the studied key soil fertility attributes. The prediction performance observed for this solution showed comparable results to the approach based on the preprogrammed measurement package tested (Geo Exploration package, Bruker AXS, Madison, WI, USA).

Tropical soil pH and sorption complex prediction via portable X-ray fluorescence spectrometry

Rapid soil fertility prediction using X-ray fluorescence data and machine learning algorithms

Portable X-Ray Fluorescence (XRF) has become increasingly popular where traditional laboratory methods are either impractical, time consuming and/or too costly. While the Limit of Detection (LOD) is generally poorer for XRF compared to laboratory-based methods, recent advances have improved XRF LODs and increased its potential for field-based studies. Portable XRF can be used to screen food products for toxic elements such as lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As), manganese, (Mn), zinc (Zn) and strontium (Sr). In this study, 23 seafood samples were analyzed using portable XRF in a home setting. After XRF measurements were completed in each home, the same samples were transferred to the laboratory for re-analysis using microwave-assisted digestion and Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS). Four elements (Mn, Sr, As and Zn) were quantifiable by XRF in most samples, and those results were compared to those obtained by ICP-MS/MS. Agreement was judged reasonable for Mn, Sr, and As, but not for Zn. Discrepancies could be due to 1) the limited time available to prepare field samples for XRF, 2) the heterogeneous nature of "real samples" analyzed by XRF, and 3) the small beam spot size (~1mm) of the XRF analyzer. Portable XRF is a cost-effective screening tool for public health investigations involving exposure to toxic metals. It is important for practitioners untrained in XRF spectrometry to (a) recognize the limitations of portable instrumentation, (b) include validation data for each specific analyte(s) measured, and (c) ensure personnel have some training in sample preparation techniques for field based XRF analyses.

Feasibility and accuracy of In Vivo and Ex Vivo XRF bone lead assessment wild birds: An example with black vultures, Coragyps atratus.

Abstract. Portable and core-scanning X-ray fluorescence (XRF) instruments have become increasingly utilized in making rapid, non-destructive chemical characterizations with high spatial resolution on a range of materials. Since basaltic cores are often highly fractured and uneven, portable XRF (pXRF) is preferred to conduct discrete chemical analyses. However, in this case, the user must select the location for each analysis, which can lead to biased datasets. Alternatively, XRF core-scanning (XRF-cs) instruments take a series of measurements along a section of core, increasing the number of analyses and, therefore, eliminating some of the bias introduced by discrete analyses conducted with a pXRF. The XRF-cs does, however, still require a flat sampling surface along the core that does not include void spaces, making rigid, vesicular, and often cracked basalts suboptimal targets. We collected 797 XRF-cs measurements on three basaltic cores collected during the International Ocean Discovery Program Expedition 396 to evaluate how effectively an XRF core scanner can build large, chemically representative datasets. We developed a method for filtering XRF-cs measurements and calibrated the data using discrete calibrated pXRF analyses and compared the XRF-cs data to pXRF and conventional bulk-rock data using various immobile (e.g., Al, Ti, Zr, Ni, Mn, Zn) and mobile (e.g., K, Ca, Sr) elements. The comparison between datasets shows that (1) the XRF-cs data reproduce trends observed by pXRF and conventional bulk-rock data at both the regional scale and the core scale, and (2) in some cases, the higher spatial resolution of the XRF-cs data reveals geochemical variations that are otherwise obscured using discrete analyses. The workflow outlined by this study can be used to select samples for future studies by efficiently providing reliable geochemical data for characterizing new and legacy hard-rock cores.