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The use of generalized linear mixed models to investigate postmortem lipids in textiles.
Collins, Sharni; Maestrini, Luca; Hui, Francis K C; Stuart, Barbara; Ueland, Maiken.
Afiliación
  • Collins S; Centre for Forensic Science, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.
  • Maestrini L; Research School of Finance, Actuarial Studies and Statistics, The Australian National University, Canberra, ACT 2601, Australia.
  • Hui FKC; Research School of Finance, Actuarial Studies and Statistics, The Australian National University, Canberra, ACT 2601, Australia.
  • Stuart B; Centre for Forensic Science, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.
  • Ueland M; Centre for Forensic Science, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.
iScience ; 26(8): 107371, 2023 Aug 18.
Article en En | MEDLINE | ID: mdl-37575194
Human remains are oftentimes located with textile materials, making them a ubiquitous source of physical evidence. Human remains are also frequently discovered in outdoor environments, increasing the exposure to scavenging activity and soft-tissue decomposition. In such cases, postmortem interval (PMI) estimations can be challenging for investigators when attempting to use traditional methods for reconstructive purposes. Lipid analysis is an emerging area of research in forensic taphonomy, with recent works demonstrating success with the detection and monitoring of lipids over time. In this work, generalized linear mixed models (GLMMs) were utilized to perform rigorous statistical analyses on 30 lipid outcomes in combination with accumulated-degree-days (ADD). The results of this study were consistent with recent works, indicating oleic and palmitic acids to be the most suitable lipids in textiles to target for future use as soft-tissue biomarkers of human decomposition. Interspecies differences between humans and pigs were also addressed in this work.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: IScience Año: 2023 Tipo del documento: Article País de afiliación: Australia

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: IScience Año: 2023 Tipo del documento: Article País de afiliación: Australia