Date
2023-10-06Subject
570 Life sciences; biology 600 Technology RöntgenfluoreszenzspektroskopiePhysikochemische BodeneigenschaftWasserstoffionenkonzentrationKationenaustauschDatenauswertungLössbodenMetadata
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Aufsatz
Deutsche Forschungsgemeinschaft
Optimization of sample preparation and data evaluation techniques for X-ray fluorescence prediction of soil texture, pH, and cation exchange capacity of loess soils
Abstract
Use of X-ray fluorescence (XRF) spectrometry for estimation of soil texture, pH, and cation exchange capacity (CEC) is desirable given the time-consuming nature of traditional methods. Recent studies have shown promising results; however, further investigation is required to determine the effects of sample preparation and data evaluation techniques on accuracy. Our objective was to compare (I) a simple but well-founded approach, combining measurement of powder samples and modeling with elemental contents as predictors in stepwise multiple linear regressions (MLR), with alternative approaches including (II) use of partial least squares regression (PLSR), (III) sample preparation as a pressed pellet, and (IV) spectral intensities as predictors (20 kV, 40 kV, and concatenated 20 + 40 kV). A total of 395 loess soils from three arable fields were used with a fivefold random training-testing approach and a hold-one-site-out training-testing approach. With random partitioning, clay, silt, and sand accuracy with approach I was excellent (ratio of performance to interquartile distance in validation (RPIQv) = 8.5–12.9), while pH and CEC estimations were satisfactory to excellent (RPIQv = 2.0–2.5 and 2.2–3.3, respectively). Differences between MLR and PLSR were negligible. Increases in accuracy with pellet samples were 1%–13% of RPIQv for 20 kV intensities, but effects were inconsistent for other predictors. The optimal predictor varied by property, and differences ranged from 3% to 13% of RPIQv. Improvements to accuracy from Approach I to the best alternative were largest for texture (10%–15%) but may be superfluous given the excellent accuracy across all approaches. Although the leave-one-site-out training resulted in variable performance, inclusion of soils from the target site in training assured reliable accuracy.
Citation
In: Soil Science Society of America Journal Volume 88 / Issue 1 (2023-10-06) , S. 27-42 ; eissn:1435-0661Sponsorship
Gefördert im Rahmen des Projekts DEALDeutsche Forschungsgemeinschaft
Citation
@article{doi:10.17170/kobra-202401299453,
author={Greenberg, Isabel and Sawallisch, Anja and Stelling, Jan and Vohland, Michael and Ludwig, Bernard},
title={Optimization of sample preparation and data evaluation techniques for X-ray fluorescence prediction of soil texture, pH, and cation exchange capacity of loess soils},
journal={Soil Science Society of America Journal},
year={2023}
}
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2024-04-08T11:14:13Z 2024-04-08T11:14:13Z 2023-10-06 doi:10.17170/kobra-202401299453 http://hdl.handle.net/123456789/15632 Gefördert im Rahmen des Projekts DEAL Deutsche Forschungsgemeinschaft eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ 570 600 Optimization of sample preparation and data evaluation techniques for X-ray fluorescence prediction of soil texture, pH, and cation exchange capacity of loess soils Aufsatz Use of X-ray fluorescence (XRF) spectrometry for estimation of soil texture, pH, and cation exchange capacity (CEC) is desirable given the time-consuming nature of traditional methods. Recent studies have shown promising results; however, further investigation is required to determine the effects of sample preparation and data evaluation techniques on accuracy. Our objective was to compare (I) a simple but well-founded approach, combining measurement of powder samples and modeling with elemental contents as predictors in stepwise multiple linear regressions (MLR), with alternative approaches including (II) use of partial least squares regression (PLSR), (III) sample preparation as a pressed pellet, and (IV) spectral intensities as predictors (20 kV, 40 kV, and concatenated 20 + 40 kV). A total of 395 loess soils from three arable fields were used with a fivefold random training-testing approach and a hold-one-site-out training-testing approach. With random partitioning, clay, silt, and sand accuracy with approach I was excellent (ratio of performance to interquartile distance in validation (RPIQv) = 8.5–12.9), while pH and CEC estimations were satisfactory to excellent (RPIQv = 2.0–2.5 and 2.2–3.3, respectively). Differences between MLR and PLSR were negligible. Increases in accuracy with pellet samples were 1%–13% of RPIQv for 20 kV intensities, but effects were inconsistent for other predictors. The optimal predictor varied by property, and differences ranged from 3% to 13% of RPIQv. Improvements to accuracy from Approach I to the best alternative were largest for texture (10%–15%) but may be superfluous given the excellent accuracy across all approaches. Although the leave-one-site-out training resulted in variable performance, inclusion of soils from the target site in training assured reliable accuracy. open access Greenberg, Isabel Sawallisch, Anja Stelling, Jan Vohland, Michael Ludwig, Bernard doi:10.1002/saj2.20598 Grant Numbers: LU 583/24-1, VO 1509/10-1 Röntgenfluoreszenzspektroskopie Physikochemische Bodeneigenschaft Wasserstoffionenkonzentration Kationenaustausch Datenauswertung Lössboden publishedVersion eissn:1435-0661 Issue 1 Soil Science Society of America Journal 27-42 Volume 88 false
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