Quick Analysis of Organic Amendments via Portable X-ray Fluorescence Spectrometry

Abstract

The determination of heavy metals in soils and organic amendments, such as compost, manure, biofertilizer, and sludge, generally involves the digestion of samples with aqua regia, and the determination of those in the solution using various techniques. Portable X-ray fluorescence (PXRF) has many advantages in relation to traditional analytical techniques. However, PXRF determines the total elemental content and, until now, its use for the analysis of organic amendments has been limited. The objective of this work is the calibration of a PXRF instrument to determine the aqua regia-soluble elemental contents directly in solid samples of organic amendments. Our proposal will avoid the digestion step and the use of other laboratory techniques. Using a training set of samples, calibration functions were obtained that allow the determination of the aqua regia-soluble contents from the PXRF readings of total contents. The calibration functions (obtained by multiple linear regression) allowed the quantitative determination of the aqua regia-soluble contents of Fe, K, P, S, Zn, Cu, Pb, Sr, Cr, and Mn, as well as the organic matter content and a semi-quantitative assessment of Al, Ca, V, Ba, Ni, and As contents. The readings of Si, Fe, Al, Ca, K, or S were used as correction factors, indicating that the calibrations functions found are truly based on the chemical composition of the sample matrix. This study will allow a fast, cheap, and reliable field analysis of organic amendments and of other biomass-based materials.

Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning

“…Either way, the results show that randomized cross validation and hold-out validations are essential to interpret model accuracy and reliability. Our results indicate that MIR + machine learning is not yet a proven method to infer Mg and Mn concentrations in OA, contradicting the study of López-Núñez et al [ 36 ] who claimed good calibrations for pXRF and a wide range of nutrients and contaminants in very similar OA (but the study did not include validations). MIR did not produce useable models for trace elements (Ni, Cu, Zn) or contaminants (Cd, Pb), while both machine learning model types produced useable models from XRF data; this is an unsurprising conclusion as these elements all have fluorescence peaks identifiable to low levels with XRF.…”

“…López-Núñez et al [36] who claimed good calibrations for pXRF and a wide range of nutrients and contaminants in very similar OA (but the study did not include validations). MIR did not produce useable models for trace elements (Ni, Cu, Zn) or contaminants (Cd, Pb), while both machine learning model types produced useable models from XRF data; this is an unsurprising conclusion as these elements all have fluorescence peaks identifiable to low levels with XRF.…”

Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable X-Ray Fluorescence Measurements

“…The results showed that Zn predictions were of high accuracy and good model agreement (Table 6). López-Núnez et al [25] showed a similar high accuracy of predicted Zn in organic amendments with a linear model. Hence, predictions of Zn concentrations with PXRF appear highly suitable.…”

Prediction of As, Cd, Cr, Hg, Ni, and Se Concentrations in Organic Amendments Using Portable X-ray Fluorescence and Multivariate Modeling

“…The Fe(m) readings further improved Equation (17). The P(m) readings showed a correlation with Se(ae) and Equations (19) and (20) used P(m). The second coefficients in these equations were Fe(m) and Ba(s).…”

“…The soil mode is based on Compton normalization and is often used for scanning and detection for soil metallic elements [4] at low concentrations (<1%). In this mode, readings were obtained for the following elements: Mo (19), Zr (29), Sr (30), Rb (30), Pb (24), As (15), Zn (30), Cu (28), Ni (12), Fe (30), Mn (26), Cr (17), V (14), Ti (29), Sc (28), Ca (30), K (30), S (30), and Ba (18). Furthermore, U (5), Th (5), Au (0), Se (0), Co (0), Hg (0), W(0), Cs (5), Te (0), Sb (1), Sn (6), Cd (0), Ag (4), and Pd (0) were included in soil mode, although they were below the LODs in almost all samples and they were not used in the following.…”