# Portable X-ray Fluorescence Analysis of Organic Amendments: A Review
## 3 Results and Discussion
### Derived Properties  
One of the advantages of the pXRF technique is that it can provide extensive information about the chemical composition of a sample. All elements present in concentrations above the LOD from Na (atomic number 11) to U (atomic number 92) can be determined in one quick scan, although special conditions, such as a vacuum, may be necessary for the lighter elements. Having this extensive information on the elemental composition of the sample allows the use of chemometric procedures to predict other physical-chemical properties of the samples. Figure 2: Performing measurements under laboratory conditions: (**a**) pXRF sample cup 25 mm high and 25 mm in diameter with protective film; (**b**) Sample cup filled with finely ground sample; (**c**) Thin protective film; (**d**) Samples in Ziploc bags; (**e**) Instrument attached to protection stand with sample positioned for measurement. As mentioned previously, in 2018, the studies by [PERSON] et al. [30] and [PERSON] et al. [24] used elemental data as a proxy for the prediction of derived properties such as compost electrical conductivity (EC) and cation exchange capacity (CEC), respectively. For the modelling of the derived properties, a principal component regression (PCR) model was used in the [PERSON] et al. study for EC and pH, while random forest (RF) regression was used in the [PERSON] et al. study for CEC. Measured vs. pXRF predicted compost EC correlated well (coefficient of determination, R\({}^{2}\) = 0.80; root mean squared error, RMSE = 1.04 dS m\({}^{-1}\)) and CEC showed excellent correlation (R\({}^{2}\) = 0.90, RMSE = 5.41 meq 100 g\({}^{-1}\), for the calibration set of samples). Compost pH was reasonably explained via pXRF (R\({}^{2}\) = 0.63, RMSE = 0.35), although the fit was not as good as for the other parameters. [PERSON] et al. [33] showed that it was also possible to determine the organic matter content of organic amendment samples from linear regression equations that use the undetermined content data (the difference to 100% of all the elements actually measured) with corrections from elements such as silicon, calcium, or iron. The same authors [36] found multiple linear regression equations that can predict _aqua regia_-soluble concentrations of the elements As, Cd, Cr, Hg, Ni, and Se using pXRF readings of other measurable elements as predictor variables. These target elements were below the LODs of the pXRF instrument used. Several amendment type-dependent singles or multiple linear functions were found based on one, two, or three predictors. The predictor readings corresponded to the concentration of elements of geogenic (Fe, Si, Ti, Cl, Zr Al, Ca, S, Mn, and Ba), anthropogenic (Zn and Pb), and agricultural (P and K) origin. The regression coefficients of these functions were r = 0.90-0.99. In a recent paper, [PERSON] et al. [37] proposed two types of machine learning methods, forest regression and extreme gradient boosting, for the prediction of ash content, total carbon, nitrogen, and several elements from both pXRF and Diffuse Reflectance Fourier-transform mid-infrared spectroscopy (DRIFT-MIR) data using 98 samples of various organic amendments. The authors found very good regressions from pXRF data for ash, total C, total N, P, K, Ca, S, and Fe (R\({}^{2}\) > 0.90) and acceptable regressions for the elements Mg, Na, and Mn (0.7 > R\({}^{2}\) > 0.9). In general, the pXRF data gave better predictions for heavier elements and the DRIFT-MIR data gave better predictions for ash, carbon, and lighter elements. The results of the previous research studies are relevant insofar as:  
1. They show the very small number of investigations performed on the chemometric applications of pXRF. 2. They show the potential of pXRF to predict various physical-chemical properties of organic amendments, particularly organic matter and nitrogen contents that are not directly measurable by this technique. Research on the prediction of derived properties has been much more extensive in the application of pXRF to soil analysis [51]. 3.