基于转移激发拉曼差异光谱和SOM集群的纸杯证据的快速和非破坏性识别
Luchuan Tian1, Hong Jiang2, Xin Zhang3
1People's Public Security University of China, Beijing, China.
Journal of forensic sciences
|November 29, 2023
概括
这项研究引入了一种新的分类模型,将移动激发拉曼差异光谱 (SERDS) 与自组织图 (SOM) 和贝叶斯优化支持向量机 (BO-SVM) 结合起来,用于准确的纸杯分析. 综合方法实现了高分类准确性,证明了其实际实用性.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 准确有效地对纸杯材料进行分类对于质量控制和回收至关重要.
- 传统方法可能耗时或破坏性,需要先进的分析技术.
- 光谱方法为材料表征提供了非破坏性分析能力.
研究的目的:
- 开发和评估纸杯样品的快速,非破坏性,高效和准确的分类模型.
- 整合转移激发拉曼差异光谱 (SERDS) 与自组织地图 (SOM) 和贝叶斯优化支持向量机 (BO-SVM).
- 在纸杯样本中识别主要成分和填充剂.
主要方法:
- 从52个纸杯样本中采集了差异拉曼数据,使用SERDS (784-785nm激发,440mW激光功率,10秒的整合时间,280-2700cm-1光谱范围).
- 主要组件分析 (PCA) 用于减少数据的维度.
- 自组织地图 (SOM) 集群被用于分组样本,其次是费舍尔线性差异分析 (FDA) 和贝叶斯优化支持向量机 (BO-SVM) 用于分类.
主要成果:
- 在纸杯中发现的主要成分是纤维素,填充剂包括滑石粉,碳酸和高.
- 基于光谱特征,SOM集群成功将样品分为七个不同的组.
- 美国食品和药物管理局的模型达到了92.3%的准确率,而BO-SVM模型达到了96.2%的准确率.
- 索姆集群有效地区分了具有不同填充物的样本,可以在明显的拉曼光谱峰和形状中看到.
结论:
- 集成的SERDS-SOM-BO-SVM模型为纸杯分类提供了一个高度准确和高效的方法.
- BO-SVM表现出优于FDA的性能,特别是在处理复杂的数据和异常值时,这表明更好的概括性.
- 这项研究突出了SOM聚类在通过拉曼光谱学识别材料组成的实际意义.
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