XGBoost算法使用拉曼光谱学辅助多组件定量分析
Qiaoyun Wang1, Xin Zou2, Yinji Chen2
1College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China.
概括
一种新的 eXtreme Gradient Boosting (XGBoost) 预处理方法增强了对葡萄糖,甘油和乙醇混合物的拉曼光谱分析. 这种方法提高了预测准确度,减少了文物,超过了传统方法.
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:
- 拉曼光谱对于分析化学混合物至关重要.
- 预处理对于减少文物和提高光谱数据预测准确度至关重要.
- 现有的预处理方法可能无法完全解决多组分光谱的复杂性.
研究的目的:
- 为拉曼光谱开发和评估一种 eXtreme Gradient Boosting (XGBoost) 预处理方法.
- 在混合溶液中定量分析葡萄糖,甘油和乙醇度.
- 评估XGBoost预处理与线性回归 (LR) 和多层感知器 (MLP) 模型相结合时的性能提升.
主要方法:
- 开发了一种用于拉曼光谱的 eXtreme Gradient Boosting (XGBoost) 预处理技术.
- 集成的XGBoost预处理与线性回归 (X-LR) 和多层感知器 (X-MLP) 模型.
- 通过比例图利用超参数调整,并使用R2,RMSEC和RMSEP指标评估模型.
主要成果:
- XGBoost预处理方法显著提高了预测性能,并减少了拉曼光谱中的工件.
- 两种X-LR和X-MLP模型都表现出增强的准确性和概括能力.
- 对于LR和MLP模型,XGBoost预处理的性能优于其他测试方法.
结论:
- 开发的XGBoost预处理方法为拉曼光谱分析提供了强大而可靠的方法.
- 这种技术有效地提高了葡萄糖,甘油和乙醇混合物的定量分析.
- XGBoost预处理代表了化学传感应用的光谱数据分析的重大进步.
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