A probabilistic approach towards source level inquiries for forensic soil examination based on mineral counts.

Bayesian approach Blind tests Forensic geology Heavy mineral fraction Inter-variability Intra-variability Light mineral fraction Multivariate statistics Polarized light microscopy Scanning electron microscopy

Journal

Forensic science international
ISSN: 1872-6283
Titre abrégé: Forensic Sci Int
Pays: Ireland
ID NLM: 7902034

Informations de publication

Date de publication:
Nov 2021
Historique:
received: 30 03 2021
accepted: 27 09 2021
pubmed: 12 10 2021
medline: 12 10 2021
entrez: 11 10 2021
Statut: ppublish

Résumé

Forensic soil examination has a well-established foundation in forensic science, this is in part due to the widely varied and complex nature of soil. Within this domain, mineral suite studies are a commonly utilized tool in soil examination. However, statistical or probabilistic approaches towards the interpretation of results from such analysis are lacking and this study aims to fill that gap. Soil samples from four different locations in the city of Lausanne, Switzerland were sampled and their mineral fractions, light and heavy of size between 90 and 180 µm, were studied utilizing microscopical methods. First, the light minerals were identified and counted by employing scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Second, the heavy minerals were identified and counted manually under a polarized light microscope (PLM). The resulting count data were subjected to various multivariate statistical treatments such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and linear discriminant analysis (LDA). These methods assist in identifying pertinent variables and subsequently in building various classification models. The validities of these models were then tested and evaluated using blind tests. Finally, these methods demonstrate how a probabilistic approach can be taken in the interpretation of the results to answer source level questions.

Identifiants

pubmed: 34634691
pii: S0379-0738(21)00355-8
doi: 10.1016/j.forsciint.2021.111035
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

111035

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Yu Chen Lim (YC)

University of Lausanne, Ecole des sciences criminelles, Batochime, 1015 Lausanne, Switzerland. Electronic address: yuchen.lim@unil.ch.

André Marolf (A)

University of Lausanne, Ecole des sciences criminelles, Batochime, 1015 Lausanne, Switzerland. Electronic address: andre.marolf@unil.ch.

Nicolas Estoppey (N)

University of Lausanne, Ecole des sciences criminelles, Batochime, 1015 Lausanne, Switzerland. Electronic address: nicolas.estoppey@unil.ch.

Geneviève Massonnet (G)

University of Lausanne, Ecole des sciences criminelles, Batochime, 1015 Lausanne, Switzerland. Electronic address: genevieve.massonnet@unil.ch.

Classifications MeSH