Optimization of sample preparation and data evaluation techniques for X-ray fluorescence prediction of soil texture, pH, and cation exchange capacity of loess soils

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dc.date.accessioned2024-04-08T11:14:13Z
dc.date.available2024-04-08T11:14:13Z
dc.date.issued2023-10-06
dc.description.sponsorshipGefördert im Rahmen des Projekts DEALger
dc.description.sponsorshipDeutsche Forschungsgemeinschaftger
dc.identifierdoi:10.17170/kobra-202401299453
dc.identifier.urihttp://hdl.handle.net/123456789/15632
dc.language.isoeng
dc.relation.doidoi:10.1002/saj2.20598
dc.relation.projectidGrant Numbers: LU 583/24-1, VO 1509/10-1ger
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.ddc570
dc.subject.ddc600
dc.subject.swdRöntgenfluoreszenzspektroskopieger
dc.subject.swdPhysikochemische Bodeneigenschaftger
dc.subject.swdWasserstoffionenkonzentrationger
dc.subject.swdKationenaustauschger
dc.subject.swdDatenauswertungger
dc.subject.swdLössbodenger
dc.titleOptimization of sample preparation and data evaluation techniques for X-ray fluorescence prediction of soil texture, pH, and cation exchange capacity of loess soilseng
dc.typeAufsatz
dc.type.versionpublishedVersion
dcterms.abstractUse 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.eng
dcterms.accessRightsopen access
dcterms.creatorGreenberg, Isabel
dcterms.creatorSawallisch, Anja
dcterms.creatorStelling, Jan
dcterms.creatorVohland, Michael
dcterms.creatorLudwig, Bernard
dcterms.source.identifiereissn:1435-0661
dcterms.source.issueIssue 1
dcterms.source.journalSoil Science Society of America Journaleng
dcterms.source.pageinfo27-42
dcterms.source.volumeVolume 88
kup.iskupfalse

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