通过NIR和MIR光谱进行体外葡萄糖测量:机器学习和过化学学的综合基准
Heydar Khadem1,2,3, Hoda Nemat1, Jackie Elliott4,5
1Department of Electronic and Electrical Engineering, University of Sheffield, UK.
Heliyon
|May 23, 2024
概括
这项研究使用光谱学对比了机器学习和预处理过器用于分析葡萄糖. 卷积移动平均和Savitzky-Golay过器与线性模型提供了最准确的葡萄糖预测.
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:
- 准确的葡萄糖量化对于医疗和工业应用至关重要.
- 选择最佳的预处理和回归工具用于光谱葡萄糖分析仍然是一个挑战.
研究的目的:
- 为了比较分析机器学习和预处理过器技术用于葡萄糖测定.
- 评估近红外 (NIR),中红外 (MIR) 和联合NIR/MIR光谱对葡萄糖预测的有效性.
- 为了确定最优的方法来准确预测葡萄糖水平.
主要方法:
- 使用NIR,MIR和联合NIR/MIR光谱学从葡萄糖溶液中获得的光谱数据.
- 应用的预处理过器:卷积移动平均线,萨维茨基-戈莱,乘法散射校正和规范化.
- 利用机器学习算法:线性建模,传统非线性建模和人工神经网络.
主要成果:
- 与非线性模型相比,线性模型显示出更高的预测准确性.
- 人工神经网络模型显示性能与线性模型相比.
- 卷积移动平均线和Savitzky-Golay过器提供了最精确的结果.
结论:
- 适当的过方法显著提高了光谱血糖测量的预测精度.
- 线性模型与特定过器相结合,为葡萄糖量化提供了一种高度有效的方法.
- 这些发现支持开发先进的葡萄糖监测技术.
相关概念视频
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