基于SERS的电子语言数据分析方法:从频谱准备到定性和定量建模
Pengju Yin1, Xiaoyao Wu1, Yumeng Xiao1
1School of Mathematics and Physics, Hebei University of Engineering, Handan, Hebei 056038, China.
ACS sensors
|March 3, 2026
概括
基于表面增强拉曼光谱 (SERS) 的电子舌头 (E-tongues) 提供非侵入性的味道分析. 本综述指导研究人员选择SERS数据的机器学习方法,解决复杂混合分析的挑战.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 表面增强拉曼光谱 (SERS) 与电子舌头 (E-tongues) 结合,提供无标签的味道分析.
- 对于复杂的液体分析,SERS-E语言利用传感阵列,光谱采集和机器学习 (ML).
- 为SERS数据提取特征和选择合适的ML方法仍然具有挑战性.
研究的目的:
- 审查基于SERS的E-tongue原则和进展情况.
- 为基于特征和目标的SERS数据提供关于科学选择ML方法的指导.
- 为SERS-E-tongue研究人员解决实际数据处理挑战.
主要方法:
- 系统审查SERS-E-tongue技术原则和研究进展.
- 对SERS数据的预处理和特征工程方法的总结和评估.
- 根据光谱数据和任务要求选择定性和定量ML算法的指导方针.
主要成果:
- 详细审查SERS-E-tongue技术及其应用.
- 数据预处理,特征工程和ML算法选择策略的系统总结.
- 现场实验室研究人员的实用方法参考书.
结论:
- 有效的ML方法选择对于SERS-E-tongue数据分析至关重要.
- 该审查为根据数据和目标选择适当的方法提供了一个框架.
- 解决目前的瓶问题,并概述SERS-E-tongue技术的未来研究方向.
关键词:
电子舌头 (E-tongue) 是一种语言.这就是 SERS SERS.数据预处理数据预处理.评估指标 评价指标液体检测器检测液体检测器机器学习算法的算法方法选择指南 方法选择指南定性分析是一种定性分析.定量分析量化分析更多相关视频
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