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.

中文翻译：

---

PC 2D-COS：二维相关光谱的主成分基础方法

本文介绍了新开发的主成分驱动二维 (2D) 相关光谱 (PC 2D-COS)，作为利用主成分分析降维的二维相关光谱的替代方法。结果表明，PC 2D-COS 等效于传统的 2D 相关分析，同时在计算复杂度和内存消耗方面具有显着优势。即使对于具有非常高光谱分辨率的数据集或对二维相关光谱的多个数据集进行并行分析，这些功能也可以轻松计算二维相关光谱。随着复杂性的降低，PC 2D-COS 通过限制用于 2D 相关计算的主要组件集，提供了显着的噪声抑制特性。作为截断 PC 2D-COS 应用的一个例子，研究了富勒烯-蒽加合物的温度相关拉曼光谱数据集。结果表明，即使对于复杂的现实世界数据集，也可以在不丢失相关信息的情况下大幅降低计算成本。