X-ray Fluorescence Sensors Research Articles

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

X-ray fluorescence (XRF) analyses are fast, clean, non-destructive, and compatible with on-field operations, which are some advantages over traditional determinations using coupled plasma optical emission spectroscopy (ICP-OES). The aim of this study was to advance in situ XRF approaches for assessing the nutritional status of soybean leaves (i.e., P, S, K, Ca, Mn, Fe, Cu and Zn). More specifically, we propose a protocol to ensure accuracy of in-field analysis and then evaluate the predictive performance of XRF via different data modelling strategies for macro- and micronutrient determination. Therefore, the XRF sensor dwell time of 60 s and the maximum time of 5 min were determined for the analysis of the leaves after leaf abscission, taking into account the influence of moisture loss on the signal intensity of the lighter elements. Regarding the predictive performance of XRF data for nutrients determination, multiple linear regression (MLR) models resulted in lower root mean square errors (RMSE) for P (433 mg kg−1), S (204 mg kg−1) and K (1957 mg kg−1); Partial least squares regression (PLS) for Ca (519 mg kg−1); and simple linear regression (SLR) for Mn (9 mg kg−1), Fe (18 mg kg−1), Zn (5 mg kg−1). The different modelling strategies exhibited equivalent RMSE for Cu (2 mg kg−1). These prediction errors are within a ±20% range, demonstrating that the in situ protocols developed in this research are useful for predicting the nutrients concentration in soybean leaves. Our study shows the possibility of using the in situ XRF sensor for the rapid and practical nutrients determination in soybean leaves, presenting good potential as a crop diagnosis tool.

Magnetic signature and X-ray fluorescence for mapping trace elements in soils originating from basalt and sandstone

The knowledge of the lithological context is necessary to interpret trace elements concentrations in the soil. Soil magnetic signature (χ) and soil X-ray fluorescence (XRF) are promising approaches in the study of the spatial variability of trace elements and the environmental monitoring of soil quality. This research aimed to assess the efficiency of measurements of χ and XRF sensors for spatial characterization of zinc (Zn), manganese (Mn), and copper (Cu) contents in soils of a sandstone-basalt transitional environment, using machine learning modeling. The studied area consisted of the Western Plateau of São Paulo (WPSP), with soils originating from sandstone and basalt. A total of 253 soil samples were collected at a depth of 0.0–0.2 m. The soils were characterized by particle size and chemical analysis: organic matter (OM), cation exchange capacity (CEC), ammonium oxalate-extracted iron (Feo), sodium dithionite-citrate-bicarbonate-extracted iron (Fed), and sulfuric acid-extracted iron (Fet). Hematite (Hm), goethite (Gt), kaolinite (Kt), and gibbsite (Gb) contents were obtained by X-ray diffraction (XRD). Magnetite (Mt) and maghemite (Mh) contents were obtained by soil χ, while trace elements contents were obtained by XRF and predicted by χ. Descriptive analysis, the test of means, and correlation were performed between attributes. Zn, Mn, and Cu contents were predicted using the machine learning algorithm random forest, and the spatial variability was obtained using the ordinary kriging interpolation technique. Landscape dissections influenced iron oxides, which had the highest contents in slightly dissected environments. Trace elements contents were not influenced by landscape dissections, demonstrating that lithological knowledge is necessary to characterize trace elements in soils. The prediction models developed through the machine learning algorithm random forest showed that χ can be used to characterize trace elements. The similar spatial pattern of trace elements obtained by XRF and χ measurements confirm the applicability of these sensors for mapping it under lithological and landscape transition, aiming for sustainable strategic planning of land use and occupation.

Deployment of XRF Sensors Underground: An Opportunity for Grade Monitoring or Bulk Ore Sorting in Cave Mines

Ore grades are monitored regularly in cave mines through drawpoint sampling. Automating grade monitoring through deploying X-ray fluorescence (XRF) sensors on the buckets of production loaders has been proposed as an alternative approach to address the issues around the traditional practice of drawpoint sampling. Bucket-mounted sensors can also be employed for bulk ore sorting underground. This study is aimed at evaluating the deployment of XRF sensors on production loaders as an opportunity for grade monitoring or bulk ore sorting in caving operations. The mill feed grade prediction performances of the drawpoint sampling program and mine planning software were assessed for the Cadia East panel cave mine. The results showed that the drawpoint samples underestimated the mill feed quality during a 10-month investigation period. The cave portions with bulk ore sorting potential were linked to the extraction level layout to estimate the number of drawpoints where sensors could be situated for diverting ore and waste. Samples obtained from the mine were tested to evaluate the ability of a lab-scale proxy of a bucket-mounted XRF sensor system to measure copper and gold grades. R-squared values of 0.84 and 0.68 were achieved between the predicted and measured copper and gold grades of the samples, respectively. Sensor test results are promising in revealing the potential to utilize XRF sensors underground. Future test work is encouraged to further validate the applicability of XRF sensors in an underground mining environment.

The effect of local samples in the accuracy of mid-infrared (MIR) and X-ray fluorescence (XRF) -based spectral prediction models

Within the soil spectroscopy community, there is an ongoing discussion addressing the comparison of the performance of prediction models built on a global calibration database, versus a local calibration database. In this study, this issue is addressed by spiking of global databases with local samples. The soil samples were analysed with MIR and XRF sensors. The samples were further measured using traditional wet chemistry methods to build the prediction models for seventeen major parameters. The prediction models applied by AgroCares, the company that assisted in this study, combine spectral information from MIR and XRF into a single ‘fused-spectrum’. The local dataset of 640 samples was split into 90% train and 10% test samples. To illustrate the benefits of using local calibration samples, three separate prediction models were built per element. For each model, 0%, 50% (randomly selected) and 100% of the local training samples were added to the global dataset. The remaining 10% local samples were used for validation. Seventeen soil parameters were selected to illustrate the differences in performance across a range of soil qualities, using the validation set to measure performance. The results showed that many models already exhibit an excellent level of performance (R2 ≥ 0.95) even without local samples. However, there was a clear trend that, as more local calibration samples were added, both R2 and ratio of performance to interquantile distance (RPIQ) increase.

Assessing soil mineralogy and weathering degree by a multi-range sensor synergistic approach: From parent rock to topsoil

The weathering degree (WD) of soils is usually based on the presence of weatherable primary minerals and the content of chemical elements along the soil profile, which traditionally demand expensive, time-consuming and environmentally unfriendly laboratory analyses (e.g. wet chemistry). To address this issue, proximal sensors often operating in only a single spectral range have been used in soil WD assessments. In this sense, we analyzed in detail the performance of proximal sensors operating at several spectral ranges to assess the soil mineralogy and WD. We selected three soil profiles developed from sedimentary rocks and analyzed samples from parent rock to topsoil. Samples from each horizon of the soil profiles and from the parent rock were collected and submitted to chemical, physical and mineralogical laboratory analyses. These samples were also analyzed using several sensors operating within different spectral ranges, i.e., gamma-ray, X-ray fluorescence (XRF), visible (VIS), infrared (IR) (near infrared, NIR, shortwave infrared, SWIR, and middle infrared, Mid-IR), and the AISA Fenix hyperspectral imaging systems (VIS-NIR-SWIR). Several WD indices were calculated using laboratory (reference) and multi-range sensor data for comparison. The spectral differences in the VIS-IR range were evaluated according to the soil mineralogy and WD. Gamma-ray and XRF sensors presented high correlation with the main soil elements in oxide minerals used in the WD quantification (R ranging from 0.70 to 0.99). We detected differences associated with the WD of each horizon of all profiles using data from VIS to Mid-IR ranges. The application of linear models to AISA images enabled the mapping and quantification of main elements along one soil profile (R2 ranging from 0.48 to 0.8) using a pixel-to-pixel approach, which facilitated the interpretation of the WD. Due to the specificity of each spectral region, the use of several sensors at different wavelengths provided a holistic overview of mineralogy and WD of the analyzed soil profiles, using a quick and non-destructive approach. This technique produces overlapping and/or complementary information of WD and mineralogy from all spectral ranges and can significantly assist traditional laboratory methods.

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches.

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.

Demonstration of an X-ray Fluorescence Sensor for the Cone Penetrometer

A feasibility test at the Naval Research Laboratory has shown that a miniature X-ray Fluorescence (XRF) sensor can be constructed for the cone penetrometer. Such a sensor would have detection limits for heavy metal contaminants in soils of a few parts per million. The next step is to demonstrate the capabilities of such a sensor in the cone penetrometer. A sensor for this purpose is being constructed and will be tested both in the laboratory and in the field. The XRF sensor will be capable of evaluating the in-situ detection limits and field screening (semi-quantitative) capabilities of x-ray fluorescence and can be transitioned to field activities for further evaluation and feedback. The sensor will consist of a miniature x-ray tube and an x-ray detector, together with appropriate apertures and x-ray filters, in a custom fabricated section of penetrometer pipe. The sensor will use commercially available components. High voltage and signal cables will couple the sensor to electronics in the truck. Boron carbide will be used for the x-ray windows which protect the sensor components from soil abrasion.