量化光谱测量误差,以指导预处理方法的选择:关于在多个NIR仪器中对大麻素预测的案例研究
Jokin Ezenarro1, Daniel Schorn-García2, Marçal Plans3
1Universitat Rovira i Virgili, ChemoSens group, Department of Analytical Chemistry and Organic Chemistry, Campus Sescelades, 43007, Tarragona, (Catalonia), Spain.
Analytica chimica acta
|February 13, 2025
概括
光谱测量错误影响近红外 (NIR) 光谱学准确度,用于大麻素预测. 新型整体错误相关指数 (IECI) 有助于优化预处理,以获得更可靠的结果.
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:
- 近红外 (NIR) 光谱对于预测化学含量至关重要.
- 在NIR光谱中测量错误可能会损害预测的准确性.
- 了解错误结构对于强大的多变量模型至关重要.
研究的目的:
- 调查用于大麻素预测的NIR光谱中的光谱测量错误.
- 为了评估不同NIR工具的错误变化.
- 引入一种用于量化错误相关性和指导预处理的新型指标.
主要方法:
- 使用NeoSpectra小型化光谱仪的案例研究.
- 对错误来源,共变率和相关性模式的分析.
- 开发和应用综合错误相关指数 (IECI).
- 评估预处理方法对错误相关性和部分最小方程 (PLS) 模型性能的影响.
主要成果:
- 光谱测量错误显著影响NIR预测的准确性.
- 该IECI指标有效量化了测量误差相关性.
- 降低IECI值的预处理方法可以提高PLS模型的性能.
- 较低的IECI值与简化和更准确的预测模型相关.
结论:
- 优化基于IECI的预处理提高了NIR光谱学对大麻素确定性的可靠性.
- IECI为管理各种测量错误提供了一个框架.
- 这项研究完善了分析化学中的多变量预测建模.
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