表面增强的拉曼光谱 - - 机器学习,用于在水中进行多重性纳弗酸剖析
Mohammadamin Rashidi1,2, Zahra Kianpoor2, Hongyan Wu2
1Department of Civil and Environmental Engineering, School of Mining and Petroleum Engineering, University of Alberta, Edmonton, Alberta T6G1H9, Canada.
Analytical chemistry
|December 10, 2025
概括
这项研究引入了一种使用表面增强拉曼光谱 (SERS) 和机器学习 (ML) 进行敏感检测水中的纳酸 (NAs) 的新方法. 该方法准确地识别和量化复杂样本中的多个NA,而无需事先分离.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 纳酸 (NAs) 是工业废水中的有毒污染物,对水生生态系统构成风险.
- 准确检测和量化NAs对于环境监测和风险评估至关重要.
研究的目的:
- 开发一种敏感的,数据驱动的方法来检测和量化水中的各种纳夫酸.
- 为了利用表面增强的拉曼光谱 (SERS) 和机器学习 (ML) 来同时识别和量化NA.
主要方法:
- 使用高度均的银 (Ag) 纳米颗粒与阴离子表面活性剂来增强纳夫酸的SERS信号.
- 采用机器学习模型,包括随机森林 (RF),回归和罗卷积神经网络 (SNN),用于光谱数据分析.
- 应用频谱转换,如快速的沃尔什-哈达马德转换 (FWHT),缩放和主要组件分析 (PCA),用于模型训练.
主要成果:
- 对八种不同的NA类型实现了低至10−4到10−5M的检测极限.
- 射频模型显示单酸识别的准确率为86.3%;脊回归模型在度预测中平均达到99.5%.
- SNN模型在复杂混合物中识别多个NA的准确率达95%,平均F1得分约为95%.
结论:
- 与ML相结合的SERS提供了一种敏感且有效的方法,用于同时识别和量化多个纳夫酸.
- 开发的方法消除了抽取或分离样本的需要,为环境监测提供了一种实际的解决方案.
- 这项工作作为一种概念证明,用于在复杂的环境矩阵中快速和灵敏地检测纳夫酸.
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