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在各种实际水中通过SERS光谱多层感知算法结合高斯核密度估计数据增强的同质多抗生素残留的识别

Zixi Huang¹, Weixin Liang², Yongqian Lei²

¹Guangdong Provincial Key Laboratory of Chemical Measurement and Emergency Test Technology, Institute of Analysis, Guangdong Academy of Sciences (China National Analytical Center, Guangzhou), Guangzhou, 510070, China; Guangdong Provincial Engineering Research Center for Online Monitoring of Water Pollution, Institute of Analysis, Guangdong Academy of Sciences (China National Analytical Center, Guangzhou), Guangzhou, 510070, China; Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou, 510006, China.

Analytica chimica acta

|December 6, 2025

概括

这项研究引入了多层感知器 (MLP) 模型与高斯核密度估计 (GKIM) 结合,用于增强表面增强拉曼光谱 (SERS) 检测抗生素残留物. 该方法可以准确地识别水样中的硫胺抗生素及其混合物,即使是在复杂的矩阵中.

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:

水中的抗生素残留物对生态系统和人类健康构成风险.
表面增强的拉曼光谱 (SERS) 可快速检测抗生素混合物.
由于复杂的矩阵和类似的目标结构,SERS检测仍然存在挑战.

研究的目的:

开发一种基于SERS的强大模型,用于在各种水类中识别多种硫胺抗生素及其混合物.
为了克服影响SERS检测准确性的矩阵干扰问题.

主要方法:

使用多层感知器 (MLP) 算法进行SERS光谱识别.
采用高斯核密度估计 (GKIM) 来增强光谱数据,特别是对于含氨和的样本.
使用液体染色学质谱法 (LC-MS) 验证了模型的性能.

主要成果:

该MLP模型实现了90%以上的准确性,F1得分和抗生素识别的召回率.
GKIM增强成功地解决了和酸盐矩阵的精度降低问题.
增强的MLP模型在真实水样中显示了93%的抗生素识别准确率.

结论:

将MLP与GKIM结合起来,可以显著改善基于SERS的各种水体中抗生素残留物的识别.
这种综合方法显示了环境监测和SERS应用的巨大潜力.

关键词:

斯核密度估计 (GKIM) 的高斯核密度估计.多层感知子 (MLP) 多层感知子 (MLP)硫胺抗生素是一种抗生素.表面的水是表面的水.表面增强拉曼散射 (SERS) 是一种

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