Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo
Ano de defesa: | 2019 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Ciências Rurais |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Ciência do Solo
|
Departamento: |
Agronomia
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/19211 |
Resumo: | Among the constituents of the soil, special attention is given to soil clay and soil organic matter (SOM), since, among other aspects, they are determinant for nutrient retention and for the formation of aggregates, which directly affect the productive potential of crops. The most commonly used methods for quantification of these constituents present some disadvantages, such as the use of chemical reagents and the generation of residues. The Near Infrared Spectroscopy (NIRS) arises as an alternative to such methods. The objective of this work is to develop models for the quantification of clay and organic matter contents in soil samples using spectral data obtained via NIRS. 400 soil samples from the UFSM routine laboratory were used for generating the calibration curve, 100 for each soil clay class (class 1 clay > 60%; class 2 clay between 41 and 60%; class 3 clay between 21 and 40%; and class 4 clay ≤ 20. Clay and organic matter contents were determined via densimeter and sulfochromic solution methods, respectively. The untreated spectra (absorbance) and the pretreated spectra (Savitzky-Golay derivative) of the 400 samples were used for calibration purposes with previously known mathematical models. For calibration, we used models with four algorithms: Multiple Linear Regression (MLR), Partial Last Squares Regression (PLSR), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). The validation of the curve was performed with the model that presented the best performance in the calibration (higher R2 and lower RMSE) and in two ways: with 40 random samples (10 of each clay class) used in the calibration; and with 200 new unknown samples (50 of each class of clay) from the UFSM routine laboratory. The clay content of the soil samples affects the predictive capacity of the calibration curve for the estimation of the SOM content via NIRS. The validation of the curves presented worse performance (higher R² and lower RMSE) when carried out on unknown samples. The model tends to overestimate the lower contents and to underestimate the higher contents of clay and SOM. Despite its potential, in order to use the prediction of these attributes via NIRS in soil analysis laboratories, further calibration studies are necessary. |
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2019-12-20T16:00:42Z2019-12-20T16:00:42Z2019-07-19http://repositorio.ufsm.br/handle/1/19211Among the constituents of the soil, special attention is given to soil clay and soil organic matter (SOM), since, among other aspects, they are determinant for nutrient retention and for the formation of aggregates, which directly affect the productive potential of crops. The most commonly used methods for quantification of these constituents present some disadvantages, such as the use of chemical reagents and the generation of residues. The Near Infrared Spectroscopy (NIRS) arises as an alternative to such methods. The objective of this work is to develop models for the quantification of clay and organic matter contents in soil samples using spectral data obtained via NIRS. 400 soil samples from the UFSM routine laboratory were used for generating the calibration curve, 100 for each soil clay class (class 1 clay > 60%; class 2 clay between 41 and 60%; class 3 clay between 21 and 40%; and class 4 clay ≤ 20. Clay and organic matter contents were determined via densimeter and sulfochromic solution methods, respectively. The untreated spectra (absorbance) and the pretreated spectra (Savitzky-Golay derivative) of the 400 samples were used for calibration purposes with previously known mathematical models. For calibration, we used models with four algorithms: Multiple Linear Regression (MLR), Partial Last Squares Regression (PLSR), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). The validation of the curve was performed with the model that presented the best performance in the calibration (higher R2 and lower RMSE) and in two ways: with 40 random samples (10 of each clay class) used in the calibration; and with 200 new unknown samples (50 of each class of clay) from the UFSM routine laboratory. The clay content of the soil samples affects the predictive capacity of the calibration curve for the estimation of the SOM content via NIRS. The validation of the curves presented worse performance (higher R² and lower RMSE) when carried out on unknown samples. The model tends to overestimate the lower contents and to underestimate the higher contents of clay and SOM. Despite its potential, in order to use the prediction of these attributes via NIRS in soil analysis laboratories, further calibration studies are necessary.Dentre os constituintes do solo, especial atenção é voltada aos teores de argila e de matéria orgânica do solo (MOS), pois, entre outros aspectos, são determinantes para retenção de nutrientes e a formação de agregados no solo, os quais afetam diretamente o potencial produtivo das culturas. Os métodos mais comumente utilizados para quantificação destes constituintes apresentam algumas desvantagens, como o uso de reagentes químicos, a geração de resíduos, a demora na execução das análises, além de serem trabalhosas. Uma alternativa a estes métodos é o uso da espectroscopia no infravermelho próximo (Near Infrared Spectroscopy – NIRS). O objetivo deste trabalho é desenvolver modelos de quantificação dos teores de argila e de matéria orgânica em amostras de solo utilizando dados espectrais por meio da técnica de espectroscopia no infravermelho próximo - NIRS. Foram utilizadas 400 amostras de solos oriundas do laboratório de rotina da UFSM, 100 para cada classe de argila do solo (classe 1 argila > 60%; classe 2 argila entre 41 e 60%; classe 3 argila entre 21 e 40% e classe 4 argila ≤ 20%), para geração de uma curva de calibração. A argila foi determinada pelo método do densímetro e a matéria orgânica por meio da solução sulfocrômica. Para calibração com modelos matemáticos previamente conhecidos, utilizou-se os espectros brutos (absorbância) e com pré-tratamento espectral (Savitzky-Golay derivative) das 400 amostras. Para calibração foram utilizados modelos com quatro algoritmos: Multiple linear regression (MLR), Partial last squares regression (PLSR), Support vector machine (SVM) e Gaussian process regression (GPR). A validação da curva foi realizada com o modelo que apresentou melhor desempenho na calibração (maior R2 e menor RMSE) e de duas maneiras: com 40 amostras aleatórias (10 de cada classe de argila) utilizadas na calibração e com outras 200 novas amostras (50 de cada classe de argila) desconhecidas oriundas do laboratório de rotina da UFSM. O teor de argila das amostras de solo afeta a capacidade preditiva da curva de calibração da estimativa do teor de MOS pelo NIRS. A validação das curvas apresentou pior desempenho (maior R² e menor RMSE) quando feita a partir de amostras desconhecidas, cujo modelo tende a superestimar os teores mais baixos e subestimar os teores mais elevados de argila e MOS. Apesar do potencial do NIRS, para que a predição destes atributos via NIRS seja utilizada em laboratórios de análises de solos, outros estudos de calibração ainda são necessários.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em Ciência do SoloUFSMBrasilAgronomiaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessCalibraçãoValidaçãoModelos matemáticosPré-tratamento espectralCalibrationValidationMathematical modelsSpectral pretreatmentCNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLOEspectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de soloNear infrared spectroscopy for estimation of clay and organic matter contents in soil samplesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisSilva, Leandro Souza dahttp://lattes.cnpq.br/2152888530643357Bueno, Jean Michel Mourahttp://lattes.cnpq.br/6826707506303568Tiecher, Taleshttp://lattes.cnpq.br/7665378790484936http://lattes.cnpq.br/9661702382616869Lazzaretti, Bruno Pedro500100000009600c2f00174-0754-4d4d-9615-065ab03f5e5398dd6955-2411-4fc0-9ddb-cc5073ca0c5aff090c05-5b1b-4b67-b57c-00bd1f46f3896d1dd5c6-b3cf-46ce-ba45-1bfface2b8d0reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGCS_2019_LAZZARETTI_BRUNO.pdfDIS_PPGCS_2019_LAZZARETTI_BRUNO.pdfDissertação de Mestradoapplication/pdf1665969http://repositorio.ufsm.br/bitstream/1/19211/1/DIS_PPGCS_2019_LAZZARETTI_BRUNO.pdf15f679742a9c25763821d50d5df150caMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo |
dc.title.alternative.eng.fl_str_mv |
Near infrared spectroscopy for estimation of clay and organic matter contents in soil samples |
title |
Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo |
spellingShingle |
Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo Lazzaretti, Bruno Pedro Calibração Validação Modelos matemáticos Pré-tratamento espectral Calibration Validation Mathematical models Spectral pretreatment CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO |
title_short |
Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo |
title_full |
Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo |
title_fullStr |
Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo |
title_full_unstemmed |
Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo |
title_sort |
Espectroscopia no infravermelho próximo para estimativa dos teores de argila e de matéria orgânica em amostras de solo |
author |
Lazzaretti, Bruno Pedro |
author_facet |
Lazzaretti, Bruno Pedro |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Silva, Leandro Souza da |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2152888530643357 |
dc.contributor.referee1.fl_str_mv |
Bueno, Jean Michel Moura |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/6826707506303568 |
dc.contributor.referee2.fl_str_mv |
Tiecher, Tales |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/7665378790484936 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/9661702382616869 |
dc.contributor.author.fl_str_mv |
Lazzaretti, Bruno Pedro |
contributor_str_mv |
Silva, Leandro Souza da Bueno, Jean Michel Moura Tiecher, Tales |
dc.subject.por.fl_str_mv |
Calibração Validação Modelos matemáticos Pré-tratamento espectral |
topic |
Calibração Validação Modelos matemáticos Pré-tratamento espectral Calibration Validation Mathematical models Spectral pretreatment CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO |
dc.subject.eng.fl_str_mv |
Calibration Validation Mathematical models Spectral pretreatment |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO |
description |
Among the constituents of the soil, special attention is given to soil clay and soil organic matter (SOM), since, among other aspects, they are determinant for nutrient retention and for the formation of aggregates, which directly affect the productive potential of crops. The most commonly used methods for quantification of these constituents present some disadvantages, such as the use of chemical reagents and the generation of residues. The Near Infrared Spectroscopy (NIRS) arises as an alternative to such methods. The objective of this work is to develop models for the quantification of clay and organic matter contents in soil samples using spectral data obtained via NIRS. 400 soil samples from the UFSM routine laboratory were used for generating the calibration curve, 100 for each soil clay class (class 1 clay > 60%; class 2 clay between 41 and 60%; class 3 clay between 21 and 40%; and class 4 clay ≤ 20. Clay and organic matter contents were determined via densimeter and sulfochromic solution methods, respectively. The untreated spectra (absorbance) and the pretreated spectra (Savitzky-Golay derivative) of the 400 samples were used for calibration purposes with previously known mathematical models. For calibration, we used models with four algorithms: Multiple Linear Regression (MLR), Partial Last Squares Regression (PLSR), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). The validation of the curve was performed with the model that presented the best performance in the calibration (higher R2 and lower RMSE) and in two ways: with 40 random samples (10 of each clay class) used in the calibration; and with 200 new unknown samples (50 of each class of clay) from the UFSM routine laboratory. The clay content of the soil samples affects the predictive capacity of the calibration curve for the estimation of the SOM content via NIRS. The validation of the curves presented worse performance (higher R² and lower RMSE) when carried out on unknown samples. The model tends to overestimate the lower contents and to underestimate the higher contents of clay and SOM. Despite its potential, in order to use the prediction of these attributes via NIRS in soil analysis laboratories, further calibration studies are necessary. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-12-20T16:00:42Z |
dc.date.available.fl_str_mv |
2019-12-20T16:00:42Z |
dc.date.issued.fl_str_mv |
2019-07-19 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/19211 |
url |
http://repositorio.ufsm.br/handle/1/19211 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
500100000009 |
dc.relation.confidence.fl_str_mv |
600 |
dc.relation.authority.fl_str_mv |
c2f00174-0754-4d4d-9615-065ab03f5e53 98dd6955-2411-4fc0-9ddb-cc5073ca0c5a ff090c05-5b1b-4b67-b57c-00bd1f46f389 6d1dd5c6-b3cf-46ce-ba45-1bfface2b8d0 |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Ciência do Solo |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Agronomia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Biblioteca Digital de Teses e Dissertações do UFSM |
collection |
Biblioteca Digital de Teses e Dissertações do UFSM |
bitstream.url.fl_str_mv |
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