The Identification of Biological Stains at Crime Scenes: A Promising Role for Proteomics and Machine Learning
Abstract
Forensic body fluid identification is crucial for reconstructing crime scene events. While DNA analysis provides individualization, it lacks information about the fluid's origin. We developed and evaluated three complementary proteomic approaches using LC-HRMS/MS to identify blood, saliva, semen, urine, and vaginal fluid, including complex mixtures. The first method utilized fluid-specific peptide biomarkers, achieving high accuracy for pure fluids. The second employed peptide abundance ratios, demonstrating effectiveness in body fluid mixtures. The third, a machine learning model using Classifier Chain Random Forest, achieved 100% accuracy for pure fluids and promising results for mixtures. Our results revealed the complementarity of different tests, with the peptide-specific biomarker and machine-learning approaches being the most robust. This study demonstrates the potential of proteomics for comprehensive body fluid identification, offering valuable tools for forensic investigations.
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BibTeX
@misc{rosenberg2026identification,
title = {{The Identification of Biological Stains at Crime Scenes: A Promising Role for Proteomics and Machine Learning}},
author = {Anna Rosenberg and Stéphanie Laurent and Esther Morandeau and Alix Munoz and Joelle Vinh},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.03521},
eprint = {2609.03521},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.03521},
}
RIS
TY - UNPB
TI - The Identification of Biological Stains at Crime Scenes: A Promising Role for Proteomics and Machine Learning
AU - Anna Rosenberg
AU - Stéphanie Laurent
AU - Esther Morandeau
AU - Alix Munoz
AU - Joelle Vinh
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.03521
UR - https://arxiv.org/abs/2609.03521
AB - Forensic body fluid identification is crucial for reconstructing crime scene events. While DNA analysis provides individualization, it lacks information about the fluid's origin. We developed and evaluated three complementary proteomic approaches using LC-HRMS/MS to identify blood, saliva, semen, urine, and vaginal fluid, including complex mixtures. The first method utilized fluid-specific peptide biomarkers, achieving high accuracy for pure fluids. The second employed peptide abundance ratios, demonstrating effectiveness in body fluid mixtures. The third, a machine learning model using Classifier Chain Random Forest, achieved 100% accuracy for pure fluids and promising results for mixtures. Our results revealed the complementarity of different tests, with the peptide-specific biomarker and machine-learning approaches being the most robust. This study demonstrates the potential of proteomics for comprehensive body fluid identification, offering valuable tools for forensic investigations.
ER -