Stable Isotope Provenance of Unidentified Deceased Migrants-A Pilot Study.
Zuzana ObertováGrzegorz SkrzypekMartin DanišíkKai RankenburgMarco CummaudoLara OlivieriDebora MazzarelliAnnalisa CappellaNoreen EvansDouglas UbelakerCristina CattaneoPublished in: Biology (2023)
In the global migration crisis, one of the challenges in the effort to identify deceased migrants is establishing their region of origin, which facilitates the search for ante mortem data to be compared with the post mortem information. This pilot study explores the potential of using stable isotope analysis to distinguish between individuals coming from West Africa and the Horn of Africa. Six individuals (four of known origin and two of unknown origin) were sampled. δ 13 C VPDB(keratin) , δ 15 N VPDB(keratin) and δ 18 O VSMOW(keratin) of hair were analysed using Elemental Analyzers coupled with Isotope Ratio Mass Spectrometry (IRMS). δ 18 O VSMOW(carbonate) and δ 13 C VPDB(carbonate) of bone were analysed using GasBench II with IRMS, while 87 Sr/ 86 Sr composition was determined in bone and dental enamel using laser ablation multi-collector inductively coupled plasma mass spectrometry. The stable isotope compositions of the individual from the Horn of Africa differed from the other individuals. The differences found between 87 Sr/ 86 Sr of enamel and bone and between δ 18 O and δ 13 C in bone and hair reflect changes in sources of food and water in accordance with regionally typical migration journeys. The analysis of multiple stable isotopes delivered promising results, allowing us to narrow down the region of origin of deceased migrants and corroborate the information about the migration journey.
Keyphrases
- mass spectrometry
- bone mineral density
- kidney transplantation
- soft tissue
- bone loss
- bone regeneration
- liquid chromatography
- gas chromatography
- postmenopausal women
- capillary electrophoresis
- high performance liquid chromatography
- high resolution
- human health
- health information
- risk assessment
- spinal cord
- big data
- spinal cord injury
- deep learning