The use of diffuse reflectance mid-infrared spectroscopy for the prediction of the concentration of chemical elements estimated by X-ray fluorescence in agricultural and grazing European soils

The aim of this study was to develop partial least-squares (PLS) regression models using diffuse reflectance Fourier transform mid-infrared (MIR) spectroscopy for the prediction of the concentration of elements in soil determined by X-ray fluorescence (XRF). A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were used for the development of models to predict concentrations of Al, As, Ba, Ca, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hf, K, La, Mg, Mn, Na, Nb, Ni, P, Pb, Rb, Sc, Si, Sr, Th, Ti, V, Y, Zn and Zr in soil using MIR spectroscopy. The results were compared with those obtained where MIR models were developed with the same soils but using the concentration of elements extracted with aqua regia (AR). The PLS models were cross-validated against the experimental log-transformed XRF values of all the elements. The calibration models were derived from a set of 1000 randomly selected calibration samples. The rest of the samples (3130) were used as an independent validation set. According to the residual predictive deviation (RPD), predictions were classified as follows: ‘‘Good quality’’, Ca (2.9), Mg (2.5), Al (2.3), Fe (2.2), Ga (2.2), Si (2.1), Na (2.0); ‘‘Indicator quality’’, V (1.9), Ni (1.9), Sc (1.9), K (1.8), Ti (1.8), Rb (1.8), Zn (1.7), Co (1.7), Zr (1.6), Cr (1.6), Sr (1.6), Y (1.6), Nb (1.6), Ba (1.5), Mn (1.5), As (1.5), Ce (1.5); ‘‘Poor quality’’, Cs (1.4), Th (1.4), P (1.4), Cu (1.4), Pb (1.3), La (1.2), Hf (1.1). Good agreement was observed between the RPD values obtained for the elements analysed in this study and those from the AR study. Despite the different elemental concentrations determined by the XRF method compared to the AR method, MIR spectroscopy was still capable of predicting elemental concentrations

The use of diffuse reflectance mid-infrared spectroscopy for the prediction of the concentration of chemical elements estimated by X-ray fluorescence in agricultural and grazing European soils .

The aim of this study was to develop partial least squares (PLS) models to predict the concentrations of 45 elements in soils extracted with aqua regia (AR) using mid-infrared (MIR) spectroscopy. A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were selected and MIR spectroscopy used for the development of models to predict Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hg, In, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, S, Sb, Sc, Se, Sn, Sr, Th, Ti, Tl, U, V, W, Y, Zn and Zr concentrations extracted by AR. From the full soil set, 1000 samples were randomly selected for the development of the calibration models, with the remaining 3130 samples used for model validation. Partial least-squares calibration models were used to relate the infrared (IR) spectra and the elemental concentrations in soils. The PLS calibrations were validated using cross validation and elements classified as a function of residual predictive deviation (RPD) values and R2 of the predictions. According to the RPD and R2 values of the validations, the 45 elements were allocated into two main groups; Group 1 (successful calibrations), 30 elements including those elements with RPD and R2 values equal or higher than 1.5 and 0.55, respectively: Ca (3.3, 0.91), Mg (2.5, 0.84), Al (2.4, 0.82), Fe (2.2, 0.79), Ga (2.2, 0.79), Co (2.1, 0.77), Sc (2.1, 0.77), Ni (2.0, 0.76), Ti (2.0, 0.75), Li (1.9, 0.73), Sr (1.9, 0.73), Cr (1.8, 0.69), Th (1.8, 0.69), K (1.8, 0.68), Be (1.7, 0.66), V (1.7, 0.63), S (1.6, 0.64), B (1.6, 0.62), Y (1.6, 0.61), Zn (1.6, 0.61), Rb (1.6, 0.61), Zr (1.6, 0.59), Na (1.5, 0.57), In (1.5, 0.57), Nb (1.5, 0.57), Cs (1.5, 0.57), Ce (1.5, 0.56), Cu (1.5, 0.56), Bi (1.5, 0.55) and Mn (1.5, 0.55); and group 2 for 15 elements with RPD and R2 values lower than 1.5 and 0.55, respectively: As (1.4, 0.52), La (1.4, 0.52), Ba (1.4, 0.52), Tl (1.4, 0.51), P (1.4, 0.46), U (1.4, 0.46), Sb (1.3, 0.46), Mo (1.3, 0.43), Pb (1.3, 0.42), Se (1.3, 0.40), Cd (1.3, 0.40), Sn (1.3, 0.39), Hg (1.2, 0.33), Ag (1.2, 0.32) and W (1.1, 0.19). The success of the PLS calibration models to predict AR extracted elemental concentrations in soils was found to be dependent on their relationships (directly or indirectly) with soil components that showed significant absorbances in the MIR region

New soil composition data for Europe and Australia: Demonstrating comparability, identifying continental-scale processes and learning lessons for global geochemical mapping.

New geochemical data from two continental-scale soil surveys in Europe and Australia are compared. Internal project standards were exchanged to assess comparability of analytical results. The total concentration of 26 oxides/elements (Al2O3, As, Ba, CaO, Ce, Co, Cr, Fe2O3, Ca, K2O, MgO, MnO, Na2O, Nb, Ni, P2O5, Pb, Rb, SiO2, Sr, Th, TiO2, V, Y, Zn, and Zr), Loss On Ignition (LOI) and pH are demonstrated to be comparable. Additionally, directly comparable data for 14 elements in an aqua regia extraction (Ag, As, Bi, Cd, Ce, Co, Cs, Cu, Fe, La, Li, Mn, Mo, and Pb) are provided for both continents. Median soil compositions are close, though generally Australian soils are depleted in all elements with the exception of SiO2 and Zr. This is interpreted to reflect the generally longer and, in places, more intense weathering in Australia. Calculation of the Chemical Index of Alteration (CIA) gives a median value of 72% for Australia compared to 60% for Europe. Element concentrations vary over 3 (and up to 5) orders of magnitude. Several elements (total As and Ni; aqua regia As, Co, Bi, Li, Pb) have a lower element concentration by a factor of 2-3 in the soils of northern Europe compared to southern Europe. The break in concentration coincides with the maximum extent of the last glaciation. The younger soils of northern Europe are more similar to the Australian soils than the older soils from southern Europe. In Australia, the central region with especially high SiO2 concentrations is commonly depleted in many elements

Prediction of the concentration of chemical elements extracted by aqua regia in agricultural and grazing European soils using diffuse reflectance mid-infrared spectroscopy

The aim of this study was to develop partial least squares (PLS) models to predict the concentrations of 45 elements in soils extracted by the aqua regia (AR) method using diffuse reflectance Fourier Transform mid-infrared (MIR; 4000–500 cm1) spectroscopy. A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were selected. From the full soil set, 1000 samples were randomly selected to develop PLS models. Cross-validation was used for model training and the remaining 3130 samples used for model testing. According to the ratio of standard deviation to root mean square error (RPD) of the predictions, the elements were allocated into two main groups; Group 1 (successful calibrations, 30 elements), including those elements with RPDP1.5 (the coefficient of determination, R2, also provided): Ca (3.3, 0.91), Mg (2.5, 0.84), Al (2.4, 0.83), Fe (2.2, 0.79), Ga (2.1, 0.78), Co (2.1, 0.77), Ni (2.0, 0.77), Sc (2.1, 0.76), Ti (2.0, 0.75), Li (1.9, 0.73), Sr (1.9, 0.72), K (1.8, 0.70), Cr (1.8, 0.70), Th (1.8, 0.69), Be (1.7, 0.66), S (1.7, 0.66), B (1.6, 0.63), Rb (1.6, 0.62), V (1.6, 0.62), Y (1.6, 0.61), Zn (1.6, 0.60), Zr (1.6, 0.59), Nb (1.5, 0.58), Ce (1.5, 0.58), Cs (1.5, 0.58), Na (1.5, 0.57), In (1.5, 0.57), Bi (1.5, 0.56), Cu (1.5, 0.55), and Mn (1.5, 0.54); and Group 2 for 15 elements with RPD values lower than 1.5: As (1.4, 0.52), Ba (1.4, 0.52), La (1.4, 0.52), Tl (1.4, 0.51), P (1.4, 0.46), U (1.4, 0.45), Sb (1.3, 0.46), Mo (1.3, 0.43), Pb (1.3, 0.42), Se (1.3, 0.40), Cd (1.3, 0.40), Sn (1.3, 0.38), Hg (1.2, 0.33), Ag (1.2, 0.32) and W (1.1, 0.19). The success of the PLS models was found to be dependent on their relationships (directly or indirectly) with MIR-active soil components

Comparing results from two continental geochemical surveys to world soil composition and deriving Predicted Empirical Global Soil (PEGS2) reference values.

Analytical data for 10 major oxides (Al2O3, CaO, Fe2O3, K2O, MgO, MnO, Na2O, P2O5, SiO2 and TiO2), 16 total trace elements (As, Ba, Ce, Co, Cr, Ga, Nb, Ni, Pb, Rb, Sr, Th, V, Y, Zn and Zr), 14 aqua regia extracted elements (Ag, As, Bi, Cd, Ce, Co, Cs, Cu, Fe, La, Li, Mn, Mo and Pb), Loss On Ignition (LOI) and pH from 3526 soil samples from two continents (Australia and Europe) are presented and compared to (1) the composition of the upper continental crust, (2) published world soil average values, and (3) data from other continental-scale soil surveys. It can be demonstrated that average upper continental crust values do not provide reliable estimates for natural concentrations of elements in soils. For many elements there exist substantial differences between published world soil averages and the median concentrations observed on two continents. Direct comparison with other continental datasets is hampered by the fact that often mean, instead of the statistically more robust median, is reported. Using a database of the worldwide distribution of lithological units, it can be demonstrated that lithology is a poor predictor of soil chemistry. Climate-related processes such as glaciation and weathering are strong modifiers of the geochemical signature inherited from bedrock during pedogenesis. To overcome existing shortcomings of predicted global or world soil geochemical reference values, we propose Preliminary Empirical Global Soil reference values based on analytical results of a representative number of soil samples from two continents (PEGS2)