Establishing the precise timeline of a crime can be challenging as current analytical techniques used suffer from many limitations and are destructive to the body fluids encountered at crime scenes. Raman spectroscopy has demonstrated excellent potential in forensic science as it provides direct information about the structural and molecular changes without the need for processing or extracting samples. However, its current applicability is limited to pure body fluids, as signals from the substrate underlying these fluids greatly influence the current models used for age estimation. In this study, we utilized Raman spectroscopy to identify selective spectral markers that delineate the bloodstain age in the presence of interfering signals from the substrate. The pure bloodstains and the bloodstains on the substrate were aged for two weeks at 21 ± 2 ℃ in the dark. Least absolute shrinkage and selection operator (LASSO) regression was employed to guide the feature selection in the presence of interference from substrates to accurately predict the bloodstain age. Substrate-specific regression models guided by an automated feature selection algorithm yielded low values of predictive root mean square error (0.207, 0.204, 0.222 h in logarithmic scale) and high R2 (0.924, 0.926, 0.913) on test data consisting of blood spectra on floor tile, facial tissue, and linoleum-polymer substrates, respectively. This framework for an automated feature selection algorithm relies entirely on pure bloodstain spectra to train substrate-specific models for estimating the age of composite (blood on substrate) spectra. The model can thus be easily applied to any new composite spectra and is highly scalable to new environments. This study demonstrates that Raman spectroscopy coupled with LASSO could serve as a reliable and nondestructive technique to determine the age of bloodstains on any surface while aiding forensic investigations in real-world scenarios.

中文翻译：

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EXPRESS：不同基材上血迹的特征选择和快速表征

确定犯罪的准确时间线可能具有挑战性，因为当前使用的分析技术存在许多局限性，并且对犯罪现场遇到的体液具有破坏性。拉曼光谱在法医学中显示出卓越的潜力，因为它无需处理或提取样品即可提供有关结构和分子变化的直接信息。然而，它目前的适用性仅限于纯体液，因为来自这些体液下方基质的信号极大地影响了当前用于年龄估计的模型。在这项研究中，我们利用拉曼光谱来识别选择性光谱标记，这些标记在存在来自基板的干扰信号的情况下描绘血迹年龄。纯血迹和基板上的血迹在21±2℃避光老化两周。使用最小绝对收缩和选择算子 (LASSO) 回归来指导存在基材干扰的特征选择，以准确预测血迹年龄。由自动特征选择算法引导的特定于基质的回归模型在由血液光谱组成的测试数据上产生低预测均方根误差值（对数标度为 0.207、0.204、0.222 小时）和高 R2（0.924、0.926、0.913）分别是地砖、面巾纸和油毡聚合物基材。这种自动特征选择算法的框架完全依赖于纯血迹光谱来训练特定于基底的模型，以估计复合（基底上的血液）光谱的年龄。因此，该模型可以轻松应用于任何新的复合光谱，并且可以高度扩展到新环境。这项研究表明，拉曼光谱与 LASSO 相结合可以作为一种可靠且无损的技术来确定任何表面上血迹的年龄，同时帮助现实世界中的法医调查。