无监督集群辅助方法通过拉曼光谱对甲醇-汽油混合物的共识定量分析
Biao Lu1, Shilong Wu2, Deliang Liu1
1School of Information and Engineering, Suzhou University, Suzhou 234000, China.
Molecules (Basel, Switzerland)
|April 13, 2024
概括
拉曼光谱与机器学习相结合,准确分析甲醇-汽油混合物. 该方法精确地分类混合物类型并量化甲醇含量,为燃料质量评估提供了可靠的方法.
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:
- 甲醇-汽油混合物被推广为环保生物燃料.
- 甲醇含量严重影响混合品质和燃烧.
- 准确的分析方法对于质量控制至关重要.
研究的目的:
- 进行甲醇-汽油混合物的定性和定量分析.
- 为此目的,评估拉曼光谱与机器学习相结合.
- 开发和比较先进的分析模型.
主要方法:
- 配置的甲醇-汽油混合物具有不同的度.
- 采用拉曼光谱技术进行数据采集.
- 使用部分最小方程差分分析 (PLS-DA) 来进行分类.
- 开发了一个共识模型,集成自组织映射 (SOM) 神经网络进行定量预测.
主要成果:
- 在分类混合物类别方面,PLS-DA实现了近100%的准确性.
- 在量化分析方面,无监督共识模型显著超过了PLS和UVE-PLS.
- 甲醇含量预测的相关系数始终超过0.98.
结论:
- 拉曼光谱是一种适合于甲醇-汽油混合物的定性和定量分析的技术.
- 开发的共识模型在预测甲醇含量方面提供了卓越的准确性.
- 拉曼光谱有望在燃料成分分析中得到更多的应用.
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