Scholarlink

<p>Terahertz spectral analysis is made to study baicalin concentration within scutellaria by enlarging spectral feature. In detail, baicalin powder is added to scutellaria and the change in terahertz spectroscopy is intensified by multiplicative scattering correction and principal component analysis. It is found that multiplicative scattering correction contributes to aligning the first principal components, which allows the prediction of active ingredient ratio with increasing accuracy. The mechanism of feature change is verified from the perspective of spectrum overlapping of independent material. Moreover, by combining the assumption of linear superposition and the algorithm of principal component analysis, the error attached to experiment results is interpreted and attributed to difference between the claimed and actual material component. The study suggests that principal component analysis plays a role in analyzing superposition of independent terahertz spectrum, which is seldom noticed in practice.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prediction of baicalin concentration within scutellaria based on terahertz spectroscopy by analyzing aligned principal component

  • 期刊论文

  • 发表日期：

    2025年5月13日

查看全文
  • Yizhang Li,
  • Yongbin Guo,
  • Zhongmin Wang,
  • Xiaodi Dong,
  • Guiyun Cao,
  • Xiuewei Yang,
  • Dongyue Han,
  • Zhaoqing Meng,
  • Feng Qi

摘要

Terahertz spectral analysis is made to study baicalin concentration within scutellaria by enlarging spectral feature. In detail, baicalin powder is added to scutellaria and the change in terahertz spectroscopy is intensified by multiplicative scattering correction and principal component analysis. It is found that multiplicative scattering correction contributes to aligning the first principal components, which allows the prediction of active ingredient ratio with increasing accuracy. The mechanism of feature change is verified from the perspective of spectrum overlapping of independent material. Moreover, by combining the assumption of linear superposition and the algorithm of principal component analysis, the error attached to experiment results is interpreted and attributed to difference between the claimed and actual material component. The study suggests that principal component analysis plays a role in analyzing superposition of independent terahertz spectrum, which is seldom noticed in practice.

Graphical abstract