Elemental assessment of vegetation via portable X-ray fluorescence (PXRF) spectrometry

McGladdery, Candice; Weindorf, David C.; Chakraborty, Somsubhra; Li, Bin; Paulette, Laura; Podar, Dorina; Pearson, Delaina; Kusi, Nana Yaw O.; Duda, Bogdan Matei

doi:10.1016/j.jenvman.2018.01.003

Cited by 66 publications

(43 citation statements)

References 42 publications

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“…Standard errors of prediction were 2.2, 2.6, and 1.5 mg kg -1 for Zn, Fe, and Se, respectively. Likewise, McGladdery et al (2018), using thatch, deciduous leaves, single species of grasses, tree bark and herbaceous plants, demonstrated that PXRF is a useful approach for elemental determination (Zn, Pb, Cd, and Fe) in vegetation samples.…”

Section: Introductionmentioning

confidence: 99%

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Portable X-Ray Fluorescence Spectroscopy for Rapid and Cost-Effective Determination of Elemental Composition of Ground Forage

et al. 2019

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The recent development of portable X-ray fluorescence spectrometers (PXRF) has created new avenues for rapid plant elemental concentration determination at reduced cost while avoiding hazardous chemicals. A few studies have indicated the potential use of PXRF for homogenous plant tissue analysis. However, there is a lack of information for analysis of heterogeneous plant samples like livestock forage, which consists of a mixture of several species and plant parts, each varying in elemental concentration. Our objective was to evaluate PXRF for forage analysis, specifically the effect of forage particle size and scan time on important elements including P, K, Ca, and Fe determination. Hay samples (n = 42) were oven dried (60°C for 3 days) and ground into three particle sizes (≤0.5 mm, 0.25–0.5 mm and 1–2 mm). Prepared samples were scanned by PXRF using a vacuum (<10 torr) without a filter. Samples were placed in cups over thin prolene X-ray film and scanned for 180 s. A subset (n = 29) were also scanned for 60 and 120 s. PXRF counts for P, K, Ca, and Fe were compared with laboratory Inductively Coupled Plasma Optical Emission Spectroscopy (ICP) determinations, using regression models. Results indicated that these elements could potentially be determined with PXRF (r2 ≥ 0.70) in heterogeneous forage samples. Relationship strength increased with decreasing particle size, however, the relationship was still strong (r2 ≥ 0.57) at the largest particle size. Scanning time did not affect the relationship with ICP concentration for any of the particle sizes evaluated. This work demonstrated that with the right sample preparation PXRF can obtain results comparable to acid digestion and ICP regardless of sample composition, and suggests the potential for in situ determinations.

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Section: Introductionmentioning

confidence: 99%

“…Along with this cost, the generation of hazardous waste is a consideration. X-ray fluorescence (XRF) could overcome many of these disadvantages of wet chemical analysis and allows quick determination of elemental concentrations at reduced cost in plant samples (Reidinger et al, 2012; Towett et al, 2016; McGladdery et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Portable X-Ray Fluorescence Spectroscopy for Rapid and Cost-Effective Determination of Elemental Composition of Ground Forage

et al. 2019

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“…Among the advances that have emerged in recent years and are potentially applicable in improving agricultural management, the portable X-ray fluorescence (pXRF) spectrometer stands out by detecting contents of chemical elements in a few seconds and without the production of sample waste (Ribeiro et al, 2017;Weindorf;Bakr;Zhu, 2014). PXRF can be used directly in the field or in the laboratory to detect total elemental content of the Periodic Table from Mg to U in soil samples and other materials (McGladdery et al, 2018;Pearson et al, 2017;Ribeiro et al, 2017;Weindorf;Bakr;Zhu, 2014). Some studies have shown good results for predicting soil chemical properties, such as pH (Sharma et al, 2014) and cation exchange capacity (Sharma et al, 2015), but few have attempted to predict available nutrient contents from pXRF analyses (Silva et al, 2017;Pelegrino et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Portable X-ray fluorescence (pXRF) spectrometry applied to the prediction of chemical attributes in Inceptisols under different land uses

et al. 2018

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Portable X-ray fluorescence (pXRF) spectrometry has been increasingly adopted for varying studies worldwide. This work aimed at characterizing effects of soil management on the content of chemical elements detected by pXRF in managed and unmanaged areas of Inceptisols, and evaluating the potential of using pXRF data to generate prediction models for soil fertility attributes, evaluating the effect of land uses on such models. Samples were collected in A, B, and C horizons of soils under native forest, native Cerrado, coffee crops with 1 and 5 years of implantation and eucalyptus. Soil fertility attributes were determined through laboratory analyses, whereas, elemental contents were obtained through pXRF analysis. PXRF data were used for modeling (regressions) and validation of soil fertility attributes and necessity of lime (NL) application, with or without distinction between managed and unmanaged areas. Management practices on coffee crops increased the levels of Sr, CaO, P2O5, Cu, and Zn. CaO content was efficient for prediction of exchangeable Ca2+ contents (R2 = 0.91), pH (R2 = 0.88), base saturation (R2 = 0.89) in managed areas. General models presented adequate results to predict exchangeable Ca2+ (R2 = 0.92), pH (R2 = 0.85), and base saturation (R2 = 0.90). Models for unmanaged areas were less effective. PXRF detected modifications in elemental contents caused by management practices and provided reliable predictions of soil fertility attributes.

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“…Recent studies have used pXRF data to predict various soil chemical and physical properties resulted from conventional laboratory analyses (Aldabaa et al, 2015;Sharma et al, 2014;Sharma et al, 2015;Silva et al, 2017;Zhu et al, 2011). This means that, analyses that are costly, difficult to be performed, time-consuming, and that generate chemical residues could be replaced or at least reduced, if accurate predictions of their results are achieved from pXRF data (Rouillon and Taylor, 2016;McGladdery et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils

Silva

Pôggere

et al. 2020

Sci. agric. (Piracicaba, Braz.)

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Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a "green chemistry" alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear regressions, and random forest algorithm, with and without incorporation of soil texture data. The modeling was developed with 70 % of the data, while the remaining 30 % was used for validation through calculation of R 2 , adjusted R 2 , root mean square error, and mean error. Simple linear regression can accurately predict SAD results of Fe 2 O 3 (R 2 0.89), TiO 2 (R 2 0.96) , and P 2 O 5 (R 2 0.89). Stepwise regressions provided accurate predictions for Al 2 O 3 (R 2 0.87) and Ki-molar weathering index (SiO 2 /Al 2 O 3) (R 2 0.74) by incorporating soil texture data, as well as for SiO 2 (R 2 0.61). Random forest also provided adequate predictions, especially for Fe 2 O 3 (R 2 0.95), and improved results of Kr-molar weathering index (SiO 2 /(Al 2 O 3 + Fe 2 O 3)) (R 2 0.66), by incorporation of soil texture data. Our findings showed that the SAD results could be accurately predicted from pXRF data, decreasing costs, time and the production of laboratory waste.

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Elemental assessment of vegetation via portable X-ray fluorescence (PXRF) spectrometry

Cited by 66 publications

References 42 publications

Portable X-Ray Fluorescence Spectroscopy for Rapid and Cost-Effective Determination of Elemental Composition of Ground Forage

Portable X-Ray Fluorescence Spectroscopy for Rapid and Cost-Effective Determination of Elemental Composition of Ground Forage

Portable X-ray fluorescence (pXRF) spectrometry applied to the prediction of chemical attributes in Inceptisols under different land uses

Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils

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