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在红外光谱中扩展乘法信号校正中使用组成光谱和加权。

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy.

机构信息

Faculty of Science and Technology, Norwegian University of Life Sciences, Drøbakveien 31, 1432 Ås, Norway.

出版信息

Molecules. 2022 Mar 15;27(6):1900. doi: 10.3390/molecules27061900.

Abstract

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.

摘要

扩展乘法信号校正(EMSC)是红外光谱中广泛使用的预处理技术。EMSC 是一种基于模型的方法,因其灵活性和多功能性而受到青睐。该模型可以通过添加组成谱来扩展,以明确地对已知分析物或干扰物进行建模。本文讨论了组成谱的使用并演示了常见的陷阱。它澄清了分析物谱和干扰物谱之间的区别,以及模型谱之间正交性的重要性。讨论了不同的归一化方法,并说明了在 EMSC 中加权的重要性。本文说明了如何估计组成分析物谱,以及如何利用它们从光谱特征中提取更多信息。结果表明,EMSC 参数可用于回归任务和分割任务。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9183/8948808/9fcee6d08c37/molecules-27-01900-g001.jpg

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