Development of a Chemometric Energy Dispersive X-Ray Fluorescence and Scattering Spectroscopy Method for Rapid Soil Quality Assessment

Sustainable land use and agricultural productivity especially in precision farming depends on the


management of soil quality and thus necessitates soil quality assessment (SQA). This calls for


cheap, simple and rapid but accurate analysis of labile micro-and macro-nutrients (herein called


soil quality indicators, SQIs). Conventional analytical methods of SQA are tedious, destructive


and expensive. This study presents the results of the systematic experimental study on the


applicability of chemometric-assisted Energy Dispersive X-ray Fluorescence (EDXRF)


spectroscopy for rapid, direct and non-destructive characterization of soils for SQA. While


EDXRF is a standard analytical technique for elemental analysis, chemometrics is a relatively


new discipline for extracting latent relationships that are concerned with physical and chemical


phenomenon from complex multivariate data. Thus the combination of EDXRF and


chemometrics affords direct transformation of X-ray spectral data to analyte concentrations and


other material properties that are implicit to complex sample matrices such as soil and


exploratory analysis, which opens up for the investigation of factors affecting soil quality.


The capabilities of the EDXRF technique have been extended beyond the classical analysis


based on fluorescence peaks by further exploiting the scatter radiation profiles obtained non-


invasively from soil samples to (i) correct for matrix effects observed in the spectrum


deconvolution of both micro- and macro-nutrient fluorescence (and scatter) radiation intensity


respectively to the concentration of selected SQIs, and (ii) to develop multivariate calibration


strategies for quantitative analysis of the macronutrients viz. Principal Component Analysis


(PCA), Partial Least Squares (PLS) regression and Artificial Neural Networks (ANNs). PCA


has been used for spectral data compression, modeling and pattern recognition for selected soils


used in this study, while PLS and ANNs have been used to design and test calibration strategies,


and quantitative analysis of selected SQIs (Fe, Cu, Zn, NO3-, SO42-, H2PO42-.) based on kaolin as


a model soil with simulated composition of Fe, Cu, Zn, NO3 -, SO42-, H2PO42-. Certified reference


materials (IAEA-Soil 7) and (IAEA-Soil 1) have been used to build spectral library for soil


classification and to perform method validation.


The developed method, hereby referred to as Energy Dispersive X-ray Fluorescence and


Scattering (EDXRFS) spectroscopy, was applied to verify the concentrations of Fe, Cu, Zn,


organic carbon (OC), N, Na, Mg, and P in soil samples from Kitale and Katumani in Kenya. The


two sites are located in high agriculturally potential areas. The macro- and micro-nutrients ‘bio-


available’ concentrations determined viz. laboratory methods of soil analyses at the National


Agricultural Research Laboratory (NARL) were considered as reference nutrient concentrations


with which the chemometric-EDXRFS generated estimates were compared.


The results of the analysis demonstrate the applicability of the method for rapid and


simultaneous SQA with good dynamic range (at trace (μg/g) levels for micronutrient (trace)


elements (Fe, Cu, and Zn) and high (%) levels for macronutrients (OC, N, Na, Mg, and P)). The


method can furnish micro- and macro-nutrient bio-available information simultaneously with no


sample pretreatments even at low signal-to-noise ratios and rapidly (sample irradiation time, 200


seconds), which is a significant reduction in routine analysis time from that for solid samples in


classical EDXRF (sample irradiation time, 2000 S). The coupling of EDXRF spectroscopy with


multivariate calibration (EDXRFS) thus allows for fast, direct and reliable predictions of


chemical SQIs in the soil models used in this study making the approach useful for routine


analysis for determination of the macro- and micro-nutrients.


The PLS and ANNs predicted nutrient concentrations compared to the NARL reference values


using a one-way ANOVA test showed no statistical difference apart from Na for PLS, at 95 %


confidence level. The performance of PLS technique was comparable with that of ANNs for the


determination of micronutrients. ANNs however performed better for the assessment of


macronutrients. Generally the results indicate that EDXRFS is a new method for complex matrix


analysis and quality assurance characterization. The method has proven to be relatively


insensitive to matrix effects, and has shown the potential to be developed for measurement of


other chemical SQIs such as pH, chemical oxygen demand (COD), bio-chemical oxygen demand


(BOD) and humus content. The ability to rapidly characterize large numbers of samples with this


technique has been demonstrated – it opens up new possibilities for SQA and generally


environmental quality assessment at an ecological scale.

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