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. 2025 Jan;47(1):87-98.
doi: 10.1007/s13258-024-01594-8. Epub 2024 Nov 6.

Bacterial profile-based body fluid identification using a machine learning approach

Sungmin Kim  1 , Han Chul Lee  2 , Jeong Eun Sim  2 , Su Jeong Park  2 , Hye Hyun Oh  2
Affiliations

Bacterial profile-based body fluid identification using a machine learning approach

Sungmin Kim et al. Genes Genomics. 2025 Jan.

Abstract

Background: Identifying the origins of biological traces is critical for the reconstruction of crime scenes in forensic investigations. Traditional methods for body fluid identification rely on chemical, enzymatic, immunological, and spectroscopic techniques, which can be sample-consuming and depend on simple color-change reactions. However, these methods have limitations when residual samples are insufficient after DNA extraction.

Objective: This study aimed to develop a method for body fluid identification by leveraging bacterial DNA profiling to overcome the limitations of the conventional approaches.

Methods: Bacterial profiles were determined by sequencing the hypervariable region of the 16 S rRNA gene, using DNA metabarcoding of evidence collected from criminal cases. Amplicon sequence variants (ASVs) were analyzed to identify significant microbial patterns in different body fluid samples.

Results: The bacterial profile-based method demonstrated high discriminatory power with a machine learning model trained using the naïve Bayes algorithm, achieving an accuracy of over 98% in classifying samples into one of four body fluid types: blood, saliva, vaginal secretion, and mixture traces of vaginal secretions and semen.

Conclusion: Bacterial profiling enhances the accuracy and robustness of body fluid identification in forensic analysis, providing a valuable alternative to traditional methods by utilizing DNA and microbial community data despite the uncontrollable conditions. This approach offers significant improvements in the classification accuracy and practical applicability in forensic investigations.

Keywords: Bacterial profiles; Body fluid identification; DNA metabarcoding; Machine learning.

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Conflict of interest statement

Declarations. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval: the Institutional Review Board of Korea National Institute for Bioethics Policy, Seoul, Korea (IRB, https://public.irb.or.kr ) (P01−202105−33−002). Informed consent: I give my consent for information about myself to be published inGenes & Genimics(GENG-D−24−00704).

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