使用红外光谱测量纤维素II:机器学习方法
Yong Ju Lee1, Soon Wan Kweon1, Tai-Ju Lee1
1Department of Forest Products and Biotechnology, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea.
International journal of biological macromolecules
|August 29, 2025
概括
这项研究引入了一种使用红外光谱的机器学习方法,以准确量化材料中的纤维素II. 这种方法提供了一种更快,更可靠的替代方法来分析纤维素异形.
科学领域:
- 材料科学
- 光谱学
- 机器学习
背景情况:
- 纤维素II异形在再生和化纤维中具有商业意义,如细胞和粘性纤维.
- 纤维素III和IV类型主要具有科学意义.
- 精确量化纤维素异形对于材料的表征至关重要.
研究的目的:
- 开发和验证一种机器学习方法来量化基于纤维素的材料中的纤维素II.
- 利用红外光谱技术进行快速可靠的纤维素II量化.
- 为了比较不同回归模型的性能.
主要方法:
- 使用红外光谱与第二衍生预处理进行增强的特征检测.
- 应用特征重要性分析以确定信息光谱区域 (1400-900 cm-1).
- 开发并比较了四种回归模型:随机森林,决策树,部分最小平方回归和多层感知器.
主要成果:
- 第二种衍生品的预处理显著改善了纤维素II特征的检测.
- 1400-900厘米-1的光谱区域对于区分纤维素异形具有高度信息性.
- 随机森林模型获得了优异的性能,R2=0.994,RMSE=0.022和MRE=0.052.
- 最好的模型成功量化了NaOH处理的木,CNC和lycell中的纤维素II.
结论:
- 红外光谱和机器学习的结合为纤维素II量化提供了强大的工具.
- 这种方法提供了一个快速可靠的替代传统X射线衍射.
- 开发的模型对于分析各种基于纤维素的材料具有很高的准确性和稳定性.
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