Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry

Tavares, Tiago Rodrigues; Nunes, Lidiane Cristina; Alves, Elton Eduardo Novais; Almeida, Eduardo de; Maldaner, Leonardo Felipe; Krug, Francisco José; Carvalho, Hudson Wallace Pereira de; Molin, José Paulo

doi:10.3390/s19235066

Cited by 25 publications

(27 citation statements)

References 29 publications

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“…Soil samples were scanned with the XRF sensor after being air-dried and sieved at 2 mm [ 35 ]. An XRF polyethylene cup of 31-mm diameter (Chemplex Industries Inc., Palm City, FL, USA) with the bottom sealed with a 4-μm thick polypropylene film (SPEX CertiPrep Inc., Metuchen, NJ, USA) was used to place ten grams of each sample.…”

Section: Methodsmentioning

confidence: 99%

“…XRF spectra allow for a broad characterization of soils’ elementary constitution, which has the potential to complement the information obtained with vis-NIR sensors [ 13 , 26 ]. The standalone use of the XRF technique has resulted in promising results for the prediction of soil texture [ 27 , 28 ], chemical attributes (e.g., pH, V, and CEC) [ 29 , 30 , 31 , 32 ], organic matter (OM) [ 33 ], and extractable nutrients (ex-K, ex-Ca, and ex-Mg) [ 24 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

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

Tavares

Molin

Javadi

et al. 2020

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tavares

Molin

Javadi

et al. 2020

Sensors

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“…Soil samples were scanned after being air-dried and sieved at 2 mm, as suggested by Tavares et al [11]. Ten grams of each sample was placed in an XRF polyethylene cup of 31 mm diameter (n. 1530, Chemplex Industries Inc., USA) sealed at the bottom with a 4-µm thick polypropylene film (n. 3520, SPEX, USA).…”

Section: Pxrf Measurements With Different Settingsmentioning

confidence: 99%

“…Reducing the analysis time of XRF sensors implies reducing the accuracy of the spectrum, due to noise increase [12]. However, fast analyses are already applied using the µ-XRF technique, which is a variant of the XRF technique, allowing for scanning the elements over the sample surface by employing a micrometric X-ray beam, which acquires spectra in high spatial density (e.g., about 350 spectra per mm 2 ) [11,55]. To make this assessment within an acceptable time, each X-ray spectrum is scanned for a very short period (e.g., 1 to 3 s per spectrum).…”

Section: Perspectives For Using Xrf Sensors As a Pss Toolmentioning

confidence: 99%

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Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

et al. 2020

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The successful use of energy-dispersive X-ray fluorescence (ED-XRF) sensors for soil analysis requires the selection of an optimal procedure of data acquisition and a simple modelling approach. This work aimed at assessing the performance of a portable XRF (XRF) sensor set up with two different X-ray tube configurations (combinations of voltage and current) to predict nine key soil fertility attributes: (clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable nutrients (P, K, Ca, and Mg). An XRF, operated at a voltage of 15 kV (and current of 23 μA) and 35 kV (and current of 7 μA), was used for analyzing 102 soil samples collected from two agricultural fields in Brazil. Two different XRF data analysis scenarios were used to build the predictive models: (i) 10 emission lines of 15 keV spectra (EL-15), and (ii) 12 emission lines of 35 keV spectra (EL-35). Multiple linear regressions (MLR) were used for model calibration, and the models’ prediction performance was evaluated using different figures of merit. The results show that although X-ray tube configuration affected the intensity of the emission lines of the different elements detected, it did not influence the prediction accuracy of the studied key fertility attributes, suggesting that both X-ray tube configurations tested can be used for future analyses. Satisfactory predictions with residual prediction deviation (RPD) ≥ 1.54 and coefficient of determination (R2) ≥ 0.61 were obtained for eight out of the ten studied soil fertility attributes (clay, OM, CEC, V, and extractable K, Ca, and Mg). In addition, simple MLR models with a limited number of emission lines was effective for practical soil analysis of the key soil fertility attributes (except pH and extractable P) using XRF. The simple and transparent methodology suggested also enables future researches that seek to optimize the XRF scanning time in order to speed up the XRF analysis in soil samples.

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In‐house manufactured benchtop XRF spectrometer

Gozetto,

de Almeida,

Brasil

et al. 2023

X-Ray Spectrometry

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The growing efficiency of agribusiness depends on the development of technology. A key challenge regards the determination of plant mineral content, which help farmers to make a decision about fertilizer use. Currently, such nutritional status diagnoses are made based on wet chemical methods; they are usually slow and generate chemical wastess. In this context, an in‐house manufactured XRF benchtop for agri‐samples is proposed in this study. This XRF system was furnished with a manual primary filter changer, vacuum chamber, dual interlock radiological protection system, which were installed in the equipment cover, and light signals. Analytical capabilities were also evaluated. The equipment presented spurious peaks of Ti, Fe, Ni, and Zn, which is not ideal for some agronomical applications; this issue might be overcome in the future by means of additional primary filters. The total x‐ray spectrum counting rate is roughly one magnitude order lower than two other commercial XRF systems, but the signal‐to‐noise ratio is quite similar. The vacuum chamber under partial vacuum improves the Al, Si, P and S signals from 1.5 to 2.2. The limits of detection (LOD, mg kg−1) for P (261), S (168), K (68), and Ca (50) macronutrients and Mn (14), Fe (15), Cu (9), and Zn (8) micronutrients are close to the ones reported in the literature. The limits of detection for P, S, K, and Ca are suitable for plant analysis.

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Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry

Cited by 25 publications

References 29 publications

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

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

Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

In‐house manufactured benchtop XRF spectrometer

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