基于机器学习的石油蒸和汽油痕迹的识别,使用从收集的样本中测量和合成的GC光谱
Omer Kaspi1,2, Yaniv Y Avissar3, Arnon Grafit3
1School of Electrical Engineering - System Engineering Program, Afeka College of Engineering, Tel Aviv, Israel.
Molecular informatics
|August 22, 2025
概括
法医专家现在可以使用新的机器学习 (ML) 工作流来使用气色谱 (GC) 来分类火灾场景中的可燃液体. 这种通过合成数据生成的方法提高了火灾调查的分类准确性和效率.
科学领域:
- 法医科学
- 分析化学
- 计算机科学
背景情况:
- 纵火调查依赖于法医专家分析气色谱 (GC) 来检测可燃液体.
- 目前的方法耗时,特别是当没有可燃液体时.
- 机器学习 (ML) 提供了一个潜在的解决方案来自动化和提高光谱分析的准确性.
研究的目的:
- 开发和验证基于ML的工作流程,使用GC染色图 (光谱) 来分类可燃液体.
- 创建一个新的光谱合成算法来增强有限的实验数据.
- 评估数据集大小和ML算法的对分类性能的影响.
主要方法:
- 从火灾现场和参考数据库收集和注释了181个真实的光谱.
- 开发了使用k-Nearest Neighbors (kNN),代表性频谱和随机森林 (RF) 算法的ML模型.
- 创建了一个光谱合成算法来生成大量的合成光谱.
- 在真实和合成数据集上训练并测试了kNN,RF,代表性频谱和深度学习 (DL) 模型.
主要成果:
- 在增强数据集上训练时,ML模型在真实光谱上获得高F1分数 (0.74-0.96).
- 性能更依赖于训练数据集的大小,而不是使用的特定ML算法.
- 在较大的合成数据集上训练的模型在独立的真实光谱上显示出更好的预测能力.
结论:
- 开发的ML工作流程和光谱合成算法有效地从GC光谱分类可燃液体.
- 增加数据集大小显著提高了法医光谱分析中的ML模型性能.
- 该方法可适应使用光谱数据的其他法医领域.
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