Chemometric Study on the Forensic Discrimination of Soil Types Using Their Infrared Spectral Characteristics

Baron, Mark; Gonzalez-Rodriguez, Jose; Croxton, Ruth; Gonzalez, Rafael; Jimenez-Perez, Rebeca

Abstract

Soil has been utilized in criminal investigations for some time because of its prevalence and transferability. It is usually the physical characteristics that are studied; however, the research carried out here aims to make use of the chemical profile of soil samples. The research we are presenting in this work used sieved (2 mm) soil samples taken from the top soil layer (about 10 cm) that were then analyzed using mid-infrared spectroscopy. The spectra obtained were pretreated and then input into two chemometric classification tools: nonlinear iterative partial least squares followed by linear discriminant analysis (NIPALS-LDA) and partial least squares discriminant analysis (PLS-DA). The models produced show that it is possible to discriminate between soil samples from different land use types and both approaches are comparable in performance. NIPALS-LDA performs much better than PLS-DA in classifying samples to location.

Citation

Baron, M., Gonzalez-Rodriguez, J., Croxton, R., Gonzalez, R., & Jimenez-Perez, R. (2011). Chemometric Study on the Forensic Discrimination of Soil Types Using Their Infrared Spectral Characteristics. Applied Spectroscopy, 65(10), 1151-1161. https://doi.org/10.1366/10-06197

• Journal Article Type: Article
• Acceptance Date: Jul 20, 2011
• Publication Date: Oct 1, 2011
• Deposit Date: Dec 18, 2019
• Publicly Available Date: Dec 24, 2019
• Journal: Applied Spectroscopy
• Print ISSN: 0003-7028
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 65
• Issue: 10
• Pages: 1151-1161
• DOI: https://doi.org/10.1366/10-06197
• Keywords: Soil analysis; Forensic science; Fourier transform infrared spectroscopy; FT-IR spectroscopy; Nonlinear iterative partial least squares; PLS; Linear discriminant analysis; LDA; Partial least squares discriminant analysis
• Public URL: https://hull-repository.worktribe.com/output/3316342
• Contract Date: Dec 18, 2019