Hniopek Julian, Schmitt Michael, Popp Jürgen, Bocklitz Thomas
Department of Spectroscopy/Imaging, Leibniz Institute of Photonic Technologies, Jena, Germany.
Institute of Physical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany.
Appl Spectrosc. 2020 Apr;74(4):460-472. doi: 10.1177/0003702819891194. Epub 2020 Feb 19.
This paper introduces the newly developed principal component powered two-dimensional (2D) correlation spectroscopy (PC 2D-COS) as an alternative approach to 2D correlation spectroscopy taking advantage of a dimensionality reduction by principal component analysis. It is shown that PC 2D-COS is equivalent to traditional 2D correlation analysis while providing a significant advantage in terms of computational complexity and memory consumption. These features allow for an easy calculation of 2D correlation spectra even for data sets with very high spectral resolution or a parallel analysis of multiple data sets of 2D correlation spectra. Along with this reduction in complexity, PC 2D-COS offers a significant noise rejection property by limiting the set of principal components used for the 2D correlation calculation. As an example for the application of truncated PC 2D-COS a temperature-dependent Raman spectroscopic data set of a fullerene-anthracene adduct is examined. It is demonstrated that a large reduction in computational cost is possible without loss of relevant information, even for complex real world data sets.
本文介绍了新开发的主成分驱动二维（2D）相关光谱法（PC 2D-COS），它是二维相关光谱法的一种替代方法，利用主成分分析进行降维。结果表明，PC 2D-COS与传统二维相关分析等效，同时在计算复杂度和内存消耗方面具有显著优势。这些特性使得即使对于具有非常高光谱分辨率的数据集或对多个二维相关光谱数据集进行并行分析，也能轻松计算二维相关光谱。随着复杂度的降低，PC 2D-COS通过限制用于二维相关计算的主成分集，具有显著的噪声抑制特性。作为截断PC 2D-COS应用的一个例子，研究了富勒烯-蒽加合物的温度依赖性拉曼光谱数据集。结果表明，即使对于复杂的实际数据集，在不损失相关信息的情况下，也能大幅降低计算成本。
