Mapping portuguese soils using spectroscopic techniques with a machine learning approach

Detalhes bibliográficos
Autor(a) principal: Stafford, Hannah
Data de Publicação: 2014
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.26/6712
Resumo: Dissertação de mestrado Erasmus Mundus para obtenção do grau de mestre em Técnicas Laboratoriais Forenses
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spelling Mapping portuguese soils using spectroscopic techniques with a machine learning approachArtificial neural networkFourier transform infrared spectroscopyMicro x-ray fluorescence spectroscopySoil analysisUV-Visible spectroscopyDissertação de mestrado Erasmus Mundus para obtenção do grau de mestre em Técnicas Laboratoriais ForensesSoil analysis is an important part of forensic science as it can provide vital links between a suspect and a crime scene based on its characteristics. The use of soil in a forensic context can be characterised into two categories: intelligence purposes or court purposes. The core basis of the comparison of sites to determine the provenance is that soil composition, type etc. vary from one place to another. The aim of this project is to ‘map’ soils and predict the location of a sample of unknown origin based on the chemometric profiles of Fourier transform infrared (FTIR) spectra, micro x-ray fluorescence profiles and visible spectra. Thirty one samples were collected in triplicate from Monsanto Park in Lisbon for each predetermined collection point on a defined grid. Full FTIR spectra (400-4000cm-1), Visible (1100-401cm-1) spectra, UV (400-200cm-1) spectra and μXRF profiles were collected for all samples. A subset of 43 discriminant features was selected from a total of 1430 using the Boruta feature selection algorithm from the FTIR, μXRF and visible spectra. These discriminant features acted as input data that was used to create a neural network which allowed the prediction of Cartesian co-ordinates (or location) of the samples with a high degree of accuracy (86%) and has shown to be a very useful approach to predict soil location.Instituto Superior de Ciências da Saúde Egas MonizFamília, CarlosFaria, MafaldaRepositório ComumStafford, Hannah2014-09-18T11:24:43Z2014-07-01T00:00:00Z2014-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.26/6712201541548enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-10-06T14:51:29Zoai:comum.rcaap.pt:10400.26/6712Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T15:06:05.368205Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Mapping portuguese soils using spectroscopic techniques with a machine learning approach
title Mapping portuguese soils using spectroscopic techniques with a machine learning approach
spellingShingle Mapping portuguese soils using spectroscopic techniques with a machine learning approach
Stafford, Hannah
Artificial neural network
Fourier transform infrared spectroscopy
Micro x-ray fluorescence spectroscopy
Soil analysis
UV-Visible spectroscopy
title_short Mapping portuguese soils using spectroscopic techniques with a machine learning approach
title_full Mapping portuguese soils using spectroscopic techniques with a machine learning approach
title_fullStr Mapping portuguese soils using spectroscopic techniques with a machine learning approach
title_full_unstemmed Mapping portuguese soils using spectroscopic techniques with a machine learning approach
title_sort Mapping portuguese soils using spectroscopic techniques with a machine learning approach
author Stafford, Hannah
author_facet Stafford, Hannah
author_role author
dc.contributor.none.fl_str_mv Família, Carlos
Faria, Mafalda
Repositório Comum
dc.contributor.author.fl_str_mv Stafford, Hannah
dc.subject.por.fl_str_mv Artificial neural network
Fourier transform infrared spectroscopy
Micro x-ray fluorescence spectroscopy
Soil analysis
UV-Visible spectroscopy
topic Artificial neural network
Fourier transform infrared spectroscopy
Micro x-ray fluorescence spectroscopy
Soil analysis
UV-Visible spectroscopy
description Dissertação de mestrado Erasmus Mundus para obtenção do grau de mestre em Técnicas Laboratoriais Forenses
publishDate 2014
dc.date.none.fl_str_mv 2014-09-18T11:24:43Z
2014-07-01T00:00:00Z
2014-07-01T00:00:00Z
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://hdl.handle.net/10400.26/6712
201541548
url http://hdl.handle.net/10400.26/6712
identifier_str_mv 201541548
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Instituto Superior de Ciências da Saúde Egas Moniz
publisher.none.fl_str_mv Instituto Superior de Ciências da Saúde Egas Moniz
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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