在犯罪现场通过拉曼光谱和机器学习识别精液痕迹
Alexey V Borisov1, Mikhail S Snegerev1, Sonivette Colón-Rodríguez2
1Laboratory of Laser Molecular Imaging and Machine Learning (LM&ML), Tomsk State University, 36 Lenina Ave., Tomsk, 634050, Russian Federation.
Scientific reports
|October 4, 2024
概括
法医科学家现在可以使用拉曼光谱仪在各种表面上识别精液污点. 新的化学测量方法通过最小化基础材料的干扰来提高准确性,帮助刑事调查.
科学领域:
- 法医科学 法医科学 法医科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 生物液体,特别是精液的污点在法医调查中至关重要.
- 目前的精液识别方法面临样本破坏和基质干扰等挑战.
- 准确的识别对于重建事件和通过DNA分析识别嫌疑人至关重要.
研究的目的:
- 开发和评估使用拉曼光谱法识别干扰基板上的精液污点的化学测量方法.
- 为了克服现有方法的局限性,特别是基质干扰和样品破坏.
- 提高法医生物流体分析的准确性和可靠性.
主要方法:
- 开发了两种化学测量方法:使用加法 (MCR-AM) 的多变量曲线分辨率和光谱复杂性降低标准.
- 拉曼光谱的应用,用于分析各种基质上的生物流体染色.
- 对两种开发的方法进行比较分析,以确定优势.
主要成果:
- 使用加法 (MCR-AM) 的多变量曲线分辨率与光谱复杂性降低方法相比显示出更高的性能.
- MCR-AM有效地隔离和识别了精液污点,即使与基质相比,液体体积分也很小.
- 开发的方法显示出对随机噪声的稳定性,提高了识别可靠性.
结论:
- 化学测量方法,特别是MCR-AM,在识别精液等生物流体污染方面取得了重大进展.
- 这些方法为传统的法医识别技术提供了一个非破坏性的,更准确的替代方案.
- 这些发现对改善性侵犯案件和其他刑事调查中的法医分析有着强烈的影响.
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