A Comprehensive Study On Hair As An Investigative Approach In Forensic Science
DOI:
https://doi.org/10.61919/kbwgzk72Keywords:
Forensic science; hair analysis; microscopy; STR; mitochondrial DNA; GC-MS/LC-MS/MS; IRMS; isotopes; toxicology; ethics.Abstract
Background: Human hair is a durable keratinized matrix frequently encountered in forensic casework and capable of retaining morphological, genetic, toxicological, and isotopic information over extended periods, yet its evidentiary value depends on validated interpretation beyond conventional microscopy. Objective: To evaluate hair as an investigative substrate using an integrated analytical framework combining morphology, nuclear and mitochondrial DNA profiling, toxicology, and stable isotope analysis, and to quantify key associations relevant to forensic interpretation. Methods: A cross-sectional observational study analyzed 120 scalp-hair samples from adult participants using light microscopy/SEM for morphological features, STR profiling from hair roots for nuclear DNA, mtDNA sequencing from hair shafts, chromatographic mass spectrometry for toxicological detection, and IRMS for δ¹³C/δ¹⁵N variability; blinded assessments, replicate testing, and inter-analyst agreement were implemented. Results: Hair roots were present in 56/120 (46.7%) samples; complete STR profiles were obtained in 49/56 (87.5%), with anagen phase strongly predicting STR success (OR 4.62, 95% CI 1.71–12.49; p=0.002). mtDNA sequencing succeeded in 110/120 (91.7%). Illicit drugs/metabolites were detected in 26/120 (21.7%) and therapeutic drugs in 17/120 (14.2%); cosmetic treatment was associated with lower detected concentrations (mean difference −18.6 pg./mg, 95% CI −29.4 to −7.8; p=0.001). Mobile participants showed higher intra-hair δ¹³C variability than sedentary participants (0.71±0.26 vs 0.42±0.18‰; p=0.004). Conclusion: Multimodal hair analysis provides robust forensic intelligence when morphology is constrained to screening and molecular/chemical methods are applied with validated protocols, bias controls, and transparent reporting of limitations.
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Copyright (c) 2026 Muhammad Ali Anjum, Abdul Mohiz, Ghulam Abbas Haral, Sameer Yaqub Nasik, Saeeda Soha, Muhammad Asif Ali, Abdullah Manzar, Ume Kalsoom, Amna Arooj, Muhammad Naveed (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.