ArticleAnalytical and bioanalytical chemistry2026
GC-MS-based volatolomics and machine learning for predicting bloodstain time since deposition.
Article in Analytical and bioanalytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Estimating the time since deposition (TSD) of bloodstains at crime scenes is critical in forensic science. Although various approaches have demonstrated feasibility and accuracy for TSD estimation, analysis of volatile organic compounds (VOCs) offers a promising alternative because it is noninvasive, rapid, and potentially cost-effective. In this study, human blood was chemically profiled using solid-phase microextraction coupled with gas chromatography-mass spectrometry (SPME-GC-MS) to investigate changes in VOC profiles over time and under different storage conditions. A total of 39 metabolites were associated with TSD. This study compared the types, abundances, and temporal patterns of VOCs among three substrates: gauze, wood, and glass. Based on 13 characteristic VOCs commonly detected in dried bloodstains across these three substrates within a 7-day period, a machine learning regression model was developed and evaluated. The optimal model was a random forest (RF) model, achieving root mean square error (RMSE), coefficient of determination (R
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