Soil characterization by near-infrared spectroscopy and principal component analysis
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista ciência agronômica (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902021000100404 |
Resumo: | ABSTRACT This research aimed to use principal component analysis (PCA) as an exploratory method for spectral data of soil absorbance from the Baturité Massif and Central Hinterland (Ceará State, Brazil) to verify the potential of the technique in soil characterization. We analyzed 46 soil samples from different areas (native and cultivated). Each sample was analyzed in two particle sizes: 2 and 0.2 mm. We obtained spectral data by near-infrared spectroscopy (NIR), selecting the 1,360-2,260 nm range (2,376 variables). We evaluated three data pretreatment methods: multiplicative scatter correction (MSC), first derivative, and second derivative of the Savitzky-Golay filter. The absorption bands observed were: 1,414 nm (C-H stretching and deformation combination), 1,450 nm (O-H associated with the carbon chain), 1,780 nm (second overtone of C-H), 1,928 nm (O-H associated with molecular water), and 2,208 nm (C-H stretch and C=O combination). The best pretreatment was verified using only the multiplicative scatter correction (MSC). Two principal components explained 98% of the data variability, being the first principal component (PC1) related to the characteristic band of moisture, with negative values in the 1,928 nm region, while the second principal component (PC2) was related to the total organic matter (OM) originating from the C-H, C=O, and N-H bonds, wavelength region 1,414 nm. The PCA allowed characterizing the samples in terms of moisture and OM contents, with emphasis on soils under irrigated agroforestry system with higher values of moisture and OM, while the soil in degradation process presented lower values for these attributes. The NIR spectroscopy, associated with data processing methods (PCA and MSC), allows identifying changes in soil attributes, such as moisture and OM. |
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Soil characterization by near-infrared spectroscopy and principal component analysisNon-destructive analysisSoil spectral responseParticle sizeABSTRACT This research aimed to use principal component analysis (PCA) as an exploratory method for spectral data of soil absorbance from the Baturité Massif and Central Hinterland (Ceará State, Brazil) to verify the potential of the technique in soil characterization. We analyzed 46 soil samples from different areas (native and cultivated). Each sample was analyzed in two particle sizes: 2 and 0.2 mm. We obtained spectral data by near-infrared spectroscopy (NIR), selecting the 1,360-2,260 nm range (2,376 variables). We evaluated three data pretreatment methods: multiplicative scatter correction (MSC), first derivative, and second derivative of the Savitzky-Golay filter. The absorption bands observed were: 1,414 nm (C-H stretching and deformation combination), 1,450 nm (O-H associated with the carbon chain), 1,780 nm (second overtone of C-H), 1,928 nm (O-H associated with molecular water), and 2,208 nm (C-H stretch and C=O combination). The best pretreatment was verified using only the multiplicative scatter correction (MSC). Two principal components explained 98% of the data variability, being the first principal component (PC1) related to the characteristic band of moisture, with negative values in the 1,928 nm region, while the second principal component (PC2) was related to the total organic matter (OM) originating from the C-H, C=O, and N-H bonds, wavelength region 1,414 nm. The PCA allowed characterizing the samples in terms of moisture and OM contents, with emphasis on soils under irrigated agroforestry system with higher values of moisture and OM, while the soil in degradation process presented lower values for these attributes. The NIR spectroscopy, associated with data processing methods (PCA and MSC), allows identifying changes in soil attributes, such as moisture and OM.Universidade Federal do Ceará2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902021000100404Revista Ciência Agronômica v.52 n.1 2021reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20210004info:eu-repo/semantics/openAccessAguiar,Maria Ivanilda deRibeiro,Livia Paulia DiasRamos,Aurea Pinto dosCardoso,Edson Lopeseng2021-06-09T00:00:00Zoai:scielo:S1806-66902021000100404Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2021-06-09T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
Soil characterization by near-infrared spectroscopy and principal component analysis |
title |
Soil characterization by near-infrared spectroscopy and principal component analysis |
spellingShingle |
Soil characterization by near-infrared spectroscopy and principal component analysis Aguiar,Maria Ivanilda de Non-destructive analysis Soil spectral response Particle size |
title_short |
Soil characterization by near-infrared spectroscopy and principal component analysis |
title_full |
Soil characterization by near-infrared spectroscopy and principal component analysis |
title_fullStr |
Soil characterization by near-infrared spectroscopy and principal component analysis |
title_full_unstemmed |
Soil characterization by near-infrared spectroscopy and principal component analysis |
title_sort |
Soil characterization by near-infrared spectroscopy and principal component analysis |
author |
Aguiar,Maria Ivanilda de |
author_facet |
Aguiar,Maria Ivanilda de Ribeiro,Livia Paulia Dias Ramos,Aurea Pinto dos Cardoso,Edson Lopes |
author_role |
author |
author2 |
Ribeiro,Livia Paulia Dias Ramos,Aurea Pinto dos Cardoso,Edson Lopes |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Aguiar,Maria Ivanilda de Ribeiro,Livia Paulia Dias Ramos,Aurea Pinto dos Cardoso,Edson Lopes |
dc.subject.por.fl_str_mv |
Non-destructive analysis Soil spectral response Particle size |
topic |
Non-destructive analysis Soil spectral response Particle size |
description |
ABSTRACT This research aimed to use principal component analysis (PCA) as an exploratory method for spectral data of soil absorbance from the Baturité Massif and Central Hinterland (Ceará State, Brazil) to verify the potential of the technique in soil characterization. We analyzed 46 soil samples from different areas (native and cultivated). Each sample was analyzed in two particle sizes: 2 and 0.2 mm. We obtained spectral data by near-infrared spectroscopy (NIR), selecting the 1,360-2,260 nm range (2,376 variables). We evaluated three data pretreatment methods: multiplicative scatter correction (MSC), first derivative, and second derivative of the Savitzky-Golay filter. The absorption bands observed were: 1,414 nm (C-H stretching and deformation combination), 1,450 nm (O-H associated with the carbon chain), 1,780 nm (second overtone of C-H), 1,928 nm (O-H associated with molecular water), and 2,208 nm (C-H stretch and C=O combination). The best pretreatment was verified using only the multiplicative scatter correction (MSC). Two principal components explained 98% of the data variability, being the first principal component (PC1) related to the characteristic band of moisture, with negative values in the 1,928 nm region, while the second principal component (PC2) was related to the total organic matter (OM) originating from the C-H, C=O, and N-H bonds, wavelength region 1,414 nm. The PCA allowed characterizing the samples in terms of moisture and OM contents, with emphasis on soils under irrigated agroforestry system with higher values of moisture and OM, while the soil in degradation process presented lower values for these attributes. The NIR spectroscopy, associated with data processing methods (PCA and MSC), allows identifying changes in soil attributes, such as moisture and OM. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
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=S1806-66902021000100404 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902021000100404 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/1806-6690.20210004 |
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 |
Universidade Federal do Ceará |
publisher.none.fl_str_mv |
Universidade Federal do Ceará |
dc.source.none.fl_str_mv |
Revista Ciência Agronômica v.52 n.1 2021 reponame:Revista ciência agronômica (Online) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Revista ciência agronômica (Online) |
collection |
Revista ciência agronômica (Online) |
repository.name.fl_str_mv |
Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
||alekdutra@ufc.br|| ccarev@ufc.br |
_version_ |
1750297490234540032 |