Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils

Sérgio Henrique Godinho Silva,Giovana Clarice Poggere,Mariana Gabriele Marcolino Gonçalves,Elen Alvarenga Silva,Nilton Curi,Alceu Linares Pádua Junior,Luiz Roberto Guimarães Guilherme

doi:10.1590/1678-992x-2018-0132

Abstract

ABSTRACT Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a “green chemistry” alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear regressions, and random forest algorithm, with and without incorporation of soil texture data. The modeling was developed with 70 % of the data, while the remaining 30 % was used for validation through calculation of R2, adjusted R2, root mean square error, and mean error. Simple linear regression can accurately predict SAD results of Fe2O3 (R2 0.89), TiO2 (R2 0.96), and P2O5 (R2 0.89). Stepwise regressions provided accurate predictions for Al2O3 (R2 0.87) and Ki - molar weathering index (SiO2/Al2O3) (R2 0.74) by incorporating soil texture data, as well as for SiO2 (R2 0.61). Random forest also provided adequate predictions, especially for Fe2O3 (R2 0.95), and improved results of Kr - molar weathering index (SiO2/(Al2O3 + Fe2O3)) (R2 0.66), by incorporation of soil texture data. Our findings showed that the SAD results could be accurately predicted from pXRF data, decreasing costs, time and the production of laboratory waste.

Highlights

This work aimed at predicting Sulfuric acid digestion analyses (SAD) results from portable X-ray fluorescence spectrometry, which is proposed as a “green chemistry” alternative to the current SAD method
Fe2O3, Al2O3, and P2O5 portable X-ray fluorescence (pXRF) contents were lower or greater than those found for SAD according to soil mineralogy and, soil texture, which presented a wide variation for the studied soils due to the diversity of parent materials and weathering degree of soils (Table 4)
Since Brazilian soils have large contents of SiO2 in the sand fraction, mainly as quartz (Brinatti et al, 2010; Kämpf et al, 2012), the SiO2 content is only accessed by pXRF, justifying its larger content than that found with SAD, similar to the results for Fe2O3 and TiO2, which are components of minerals occurring in the sand fraction, such as magnetite, rutile, and ilmenite (Kämpf et al, 2012)

Summary

Introduction

Recent studies have used pXRF data to predict various soil chemical and physical properties resulted from conventional laboratory analyses (Aldabaa et al, 2015; Sharma et al, 2014; Sharma et al, 2015; Silva et al, 2017; Zhu et al, 2011). In Brazil, sulfuric acid digestion analyses (SAD) are important for studies concerning geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, as well as mineralogical composition of the soil clay fraction, among others (Curi and Kämpf, 2012) These analyses provide contents of some elements expressed on the oxide basis (Al2O3, SiO2, Fe2O3, TiO2, and P2O5). Several more robust statistical models have been used in other studies, generating suitable results, such as multiple linear regressions (Rourke et al, 2016; Forkuor et al, 2017) and random forest algorithm (Chagas et al, 2016; Souza et al, 2016; Silva et al, 2017)

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