Publication
Mapping portuguese soils using spectroscopic techniques with a machine learning approach
| dc.contributor.advisor | Família, Carlos | |
| dc.contributor.advisor | Faria, Mafalda | |
| dc.contributor.author | Stafford, Hannah | |
| dc.date.accessioned | 2014-09-18T11:24:43Z | |
| dc.date.available | 2014-09-18T11:24:43Z | |
| dc.date.issued | 2014-07 | |
| dc.description | Dissertação de mestrado Erasmus Mundus para obtenção do grau de mestre em Técnicas Laboratoriais Forenses | por |
| dc.description.abstract | Soil 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. | por |
| dc.identifier.tid | 201541548 | |
| dc.identifier.uri | http://hdl.handle.net/10400.26/6712 | |
| dc.language.iso | eng | por |
| dc.publisher | Instituto Superior de Ciências da Saúde Egas Moniz | por |
| dc.subject | Artificial neural network | por |
| dc.subject | Fourier transform infrared spectroscopy | por |
| dc.subject | Micro x-ray fluorescence spectroscopy | por |
| dc.subject | Soil analysis | por |
| dc.subject | UV-Visible spectroscopy | por |
| dc.title | Mapping portuguese soils using spectroscopic techniques with a machine learning approach | por |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | por |
| rcaap.type | masterThesis | por |