现代分析数据的相关性分析 - - 对光谱和色谱变量进行化学剖析
Gabriely S Folli1, Ellisson H de Paulo1, Francine D Santos1
1Laboratory of Chemometrics, Center of Competence in Petroleum Chemistry - NCQP, Laboratory of Research and Development of Methodologies for Analysis of Oils - LabPetro, Exact Sciences Center, Federal University of Espírito Santo (UFES), Vitória, Espírito Santo 29075-910, Brazil. paulo.filgueiras@ufes.br.
Analytical methods : advancing methods and applications
|August 25, 2023
概括
本研究评估了定量分析的各种分析技术中的多对线性. 近红外 (NIR) 和中红外 (MIR) 光谱显示高相关性,使它们适合部分最小方程 (PLS) 建模.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 多变量校准,特别是部分最小平方 (PLS),是分析具有高多对线性复杂化学数据的标准.
- 虽然对于近红外 (NIR) 和中红外 (MIR) 光谱等技术有效,但PLS可能会在变量之间的相关性较低的矩阵中扎.
- 某些分析技术可能需要替代或补充方法来克服模拟相关信息的局限性.
研究的目的:
- 评估跨多种分析技术的多线性和信息相关性.
- 根据每个技术固有的数据特征来确定部分最小平方 (PLS) 对于定量分析的适用性.
- 为不同的光谱和光谱数据类型选择适当的化学测量方法提供指导.
主要方法:
- 使用描述性统计数据评估多对线性,包括确定系数 (R2).
- 主要组件分析 (PCA) 的应用,以可视化和量化数据集内相关信息的分布.
- 对高温气体染色学 (HTGC),NIR,MIR,1H核磁共振 (1H NMR),13C核磁共振 (13C NMR) 和电喷离子化里埃转换离子循环电子共振质谱 (ESI) (±) FT-ICR MS) 的数据进行比较分析.
主要成果:
- 近红外 (NIR) 和中红外 (MIR) 光谱显示出高度的可变相关性.
- 13C核磁共振 (13C NMR) 和ESI (±) FT-ICR质谱 (MS) 呈现出更离散,更少相关的数据配置文件.
- 1H核磁共振 (1H NMR) 数据也显示了有效的PLS建模的潜力.
结论:
- 部分最小平方 (PLS) 非常适合对NIR,MIR和1HNMR数据进行定量分析,因为它们固有的多线性.
- 像13C核磁共振和质谱学这样的技术,具有较少相关变量的特点,可能会从与PLS结合的替代算法或变量选择策略中受益.
- 了解数据相关性对于优化多变量定量分析和选择适当的化学测量模型至关重要.
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