传统的基于人工智能的光谱数据处理和分类方法与传统的基于人工智能的光谱数据处理和分类方法,以提高LIBS的分析性能.
Zakaria E Ahmed1, Rania M Abdelazeem2, Mahmoud Abdelhamid3
1Central Administration for Counterfeiting and Forgery Research, Forensic Medicine Authority, Ministry of Justice, Cairo, Egypt. rabdelazeem@niles.cu.edu.eg.
Analytical methods : advancing methods and applications
|March 17, 2025
概括
人工智能 (AI) 增强了激光诱导分解光谱 (LIBS) 用于法医分析. 与传统方法相比,人工智能驱动的LIBS显著提高了调色剂样本歧视精度.
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:
- 激光诱导分解光谱 (LIBS) 是一种有价值的材料分析技术.
- 传统的LIBS数据处理方法,如主要组件分析 (PCA) 和部分最小平方区分分析 (PLS-DA),通常需要广泛的用户预处理.
- 需要更有效,更准确的方法来解释LIBS数据,特别是在取样歧视等法医应用中.
研究的目的:
- 开发和评估一种基于人工智能 (AI) 的新方法来处理和解释激光诱导分解光谱 (LIBS) 数据.
- 为了比较人工智能开发的方法与传统技术 (PCA,PLS-DA) 的性能,用于法医调色剂样本的歧视.
- 展示AI在提高法医应用的光谱分析的准确性和效率方面的潜力.
主要方法:
- 提出了一种由人工智能开发的新方法,集成正常化,插值和峰值检测用于自动化的LIBS光谱分析.
- 人工智能方法与传统的主要组件分析 (PCA) 和部分最小平方差异分析 (PLS-DA) 相比较.
- 绩效评估涉及到定量统计分析,包括精度差异百分比,差异分析,对对 t 测试和交叉验证.
主要成果:
- 人工智能开发的方法在区分来自各种打印机和复印机品牌和型号的色墨样本方面表现出卓越的性能.
- 与传统的LIBS数据分析方法相比,AI方法显著提高了准确性.
- 拟议的方法不需要用户预处理,简化了LIBS光谱分析工作流.
结论:
- 人工智能显著增强了激光诱导分解光谱用于法医应用,特别是在样本歧视中.
- 这种由人工智能开发的新方法在分析LIBS数据时提供了更高的效率和准确性,消除了对用户预处理的需求.
- 这项研究强调了人工智能在推进法医科学和其他分析领域的光谱技术方面的变革潜力.
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