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

Detalhes bibliográficos
Autor(a) principal: Silva,Sérgio Henrique Godinho
Data de Publicação: 2020
Outros Autores: Silva,Elen Alvarenga, Poggere,Giovana Clarice, Pádua Junior,Alceu Linares, Gonçalves,Mariana Gabriele Marcolino, Guilherme,Luiz Roberto Guimarães, Curi,Nilton
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000401402
Resumo: 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.
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spelling Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soilssoil clay fractionweathering indicesrandom forestproximal sensorsgreen chemistryABSTRACT 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.Escola Superior de Agricultura "Luiz de Queiroz"2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000401402Scientia Agricola v.77 n.4 2020reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2018-0132info:eu-repo/semantics/openAccessSilva,Sérgio Henrique GodinhoSilva,Elen AlvarengaPoggere,Giovana ClaricePádua Junior,Alceu LinaresGonçalves,Mariana Gabriele MarcolinoGuilherme,Luiz Roberto GuimarãesCuri,Niltoneng2019-10-30T00:00:00Zoai:scielo:S0103-90162020000401402Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2019-10-30T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils
title Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils
spellingShingle Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils
Silva,Sérgio Henrique Godinho
soil clay fraction
weathering indices
random forest
proximal sensors
green chemistry
title_short Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils
title_full Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils
title_fullStr Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils
title_full_unstemmed Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils
title_sort Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils
author Silva,Sérgio Henrique Godinho
author_facet Silva,Sérgio Henrique Godinho
Silva,Elen Alvarenga
Poggere,Giovana Clarice
Pádua Junior,Alceu Linares
Gonçalves,Mariana Gabriele Marcolino
Guilherme,Luiz Roberto Guimarães
Curi,Nilton
author_role author
author2 Silva,Elen Alvarenga
Poggere,Giovana Clarice
Pádua Junior,Alceu Linares
Gonçalves,Mariana Gabriele Marcolino
Guilherme,Luiz Roberto Guimarães
Curi,Nilton
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Silva,Sérgio Henrique Godinho
Silva,Elen Alvarenga
Poggere,Giovana Clarice
Pádua Junior,Alceu Linares
Gonçalves,Mariana Gabriele Marcolino
Guilherme,Luiz Roberto Guimarães
Curi,Nilton
dc.subject.por.fl_str_mv soil clay fraction
weathering indices
random forest
proximal sensors
green chemistry
topic soil clay fraction
weathering indices
random forest
proximal sensors
green chemistry
description 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.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000401402
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000401402
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-992x-2018-0132
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
dc.source.none.fl_str_mv Scientia Agricola v.77 n.4 2020
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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