拉曼光谱和机器学习用于法医文档检查
Yong Ju Lee1, Chang Woo Jeong2, Hong Taek Kim3
1Department of Forest Products and Biotechnology, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea. hyjikim@kookmin.ac.kr.
The Analyst
|April 1, 2025
概括
拉曼光谱学与机器学习相结合,将文档文件分类为法医分析. 这种方法提高了识别纸张来源的准确性和解释性,这对于欺诈调查至关重要.
科学领域:
- 法医科学 法医科学 法医科学
- 分析化学 分析化学
- 计算机科学 计算机科学
背景情况:
- 文档纸张分析在法医科学中对于检测伪造和欺诈至关重要.
- 传统的纸张分类方法可能耗时且主观.
- 开发客观和有效的纸张差异化方法至关重要.
研究的目的:
- 用机器学习集成的拉曼光谱学来对文档文件进行分类.
- 为此分类任务评估不同机器学习模型 (随机森林,SVM,FNN) 的性能.
- 确定关键的光谱区域和预处理技术,以提高分类准确性.
主要方法:
- 拉曼光谱法用于从文档文件中获取光谱数据.
- 机器学习模型,包括随机森林 (RF),支持矢量机器 (SVM) 和前神经网络 (FNN),都被训练在光谱数据上.
- 频谱数据经过预处理,包括第一个导数转换,并通过专注于200-1650厘米-1范围来减少维度.
主要成果:
- 随机森林模型在识别重要的光谱特征和区域方面表现出有效性.
- 第一个衍生谱预处理显著改善了跨模型的分类性能.
- 送神经网络模型实现了最高的分类准确性,F1得分为0.968.8.
- 200-1650厘米-1的信息光谱范围减少了输入变量,同时提高了模型的准确性.
结论:
- 将拉曼光谱与机器学习相结合,为法医文档检查提供了一种可解释,高效和强大的方法.
- 该研究强调了这种综合方法在准确可靠的纸张分类方面的潜力.
- 这种技术为法医文档分析提供了有希望的进步,有助于欺诈和伪造调查.
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