通过表面增强拉曼光谱的机器分类来确定微量有机污染物的度
Vishnu Jayaprakash1, Jae Bem You2, Chiranjeevi Kanike1
1Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.
Environmental science & technology
|January 25, 2024
概括
机器学习模型现在可以使用表面增强拉曼光谱 (SERS) 数据准确预测化学污染物度. 这种方法克服了以前的局限性,使得即使在样本条件不同的情况下,也可以进行可靠的量化.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 数据科学数据科学数据科学
背景情况:
- 表面增强拉曼光谱 (SERS) 是一种用于检测化学污染物的敏感技术.
- 使用SERS量化污染物度是具有挑战性的,因为光谱的变化.
- 机器学习以前已经应用于基于SERS的化合物识别.
研究的目的:
- 开发和验证一种机器学习方法,用于从SERS频谱中预测化学污染物度.
- 评估光谱预处理技术对预测准确性的影响.
- 为了评估不同数据集大小和样本变异的模型性能.
主要方法:
- 收集了三种分析物的SERS光谱数据:罗达胺6G,pyrifos和triclosan.
- 应用频域转换 (富里埃和沃尔什-哈达马德) 到光谱数据.
- 训练有素的标准和深度学习机器学习模型用于度预测.
- 在各种条件中使用交叉验证验证模型性能.
主要成果:
- 机器学习模型在从原始SERS数据中预测污染物度时实现了>80%的交叉验证准确性.
- 深度学习模型在中等大小的数据集上达到85%的准确性,在较小的数据集上达到70-80%.
- 里埃和哈达马德变换始终提高了预测准确度.
- 尽管样品准备和环境介质的变化,模型性能仍然稳健.
结论:
- 开发的机器学习方法有效地使用SERS频谱量化污染物度.
- 频域转换提高了基于SERS的定量分析的可靠性.
- 这种方法为化学污染物监测提供了一个强大的解决方案,克服了SERS量化以前的局限性.
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