» Articles » PMID: 30350957

Estimation of Age of Bloodstains by Mass-Spectrometry: A Metabolomic Approach

Overview
Journal Anal Chem
Specialty Chemistry
Date 2018 Oct 24
PMID 30350957
Citations 7
Authors
Affiliations
Soon will be listed here.
Abstract

Bloodstains are common evidence in crime scenes, containing significant information, including genetic information. Although efforts have been made to reliably determine the time of incident by analyzing the elapsed time of the bloodstain, there has been limited success. To identify candidate metabolites in bloodstains over time, we prepared bloodstain samples using filter paper and analyzed the metabolites by high-performance liquid chromatography-mass spectrometry (HPLC-MS)/MS over a 21-day period. Using Venn diagrams and by multivariate analysis, we selected 62 candidate molecular features. We found by partial least-squares discriminant analysis (PLS-DA) that the group can be classified with an accuracy of 75.0%, and the R and Q values were 0.7513 and 0.6998, respectively. Five metabolites were successfully identified based on candidate molecular features. The level of two metabolites, l-tryptophan and ergothioneine, decreased with time. The concentration of candidate metabolites that we propose reliably increased or decreased with time, thus, enabling the measurement of elapsed time of the bloodstain. This study is the first to identify markers used to analyze the elapsed time of bloodstains through metabolomics analysis.

Citing Articles

Investigation into temporal changes in the human bloodstain lipidome.

Sun W, Huang A, Wen S, Kong Q, Liu X Int J Legal Med. 2024; 139(1):303-317.

PMID: 39249528 DOI: 10.1007/s00414-024-03330-z.


Raman Spectroscopy for the Time since Deposition Estimation of a Menstrual Bloodstain.

Weber A, Wojtowicz A, Wietecha-Posluszny R, Lednev I Sensors (Basel). 2024; 24(11).

PMID: 38894054 PMC: 11174499. DOI: 10.3390/s24113262.


Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification.

Ren P, Zhou R, Li Y, Xiong S, Han B ACS Omega. 2023; 8(33):30421-30431.

PMID: 37636956 PMC: 10448484. DOI: 10.1021/acsomega.3c03572.


Validation of the Metabolite Ergothioneine as a Forensic Marker in Bloodstains.

Lee S, Mun S, Lee Y, Lee J, Kang H Molecules. 2022; 27(24).

PMID: 36558018 PMC: 9786767. DOI: 10.3390/molecules27248885.


Discovery of donor age markers from bloodstain by LC-MS/MS using a metabolic approach.

Kim H, Lee Y, Lee S, Kwon S, Chun Y, Hyun S Int J Legal Med. 2021; 136(1):297-308.

PMID: 34218338 DOI: 10.1007/s00414-021-02640-w.