Abstract

The paper proposes an overview of the primary applications of generalized two-dimensional correlation spectroscopy (2D-COS) to analytical chemistry. 2D-COS is a method used to analyze datasets obtained by spectroscopic measurements. This approach is based on the use of 2D correlation maps to detect and analyze correlations between different spectral regions or data from two measurement methods. The objective of employing 2D-COS is to enhance the volume of analytical information by uncovering latent correlations within the data. Analysis of such correlations for series of spectral data, acquired over a range of analyte concentrations, pH values, or component ratios in mixtures, and also under varying temperatures or other external factors, enables investigating and elucidating chemical processes and interactions. This information is not readily attainable directly from the spectra. In comparison to one-dimensional spectra, 2D-COS excels in the significance of analytical information, particularly in the identification of components and the determination of the composition of complex mixtures. 2D-COS can also be used for monitoring changes in a sample over time, which makes it a valuable tool for studying dynamically evolving systems. Overall, 2D-COS is a sufficiently versatile approach that can be employed in conjunction with numerous methods, addressing a wide range of analytical tasks and complex samples, also without sample preparation. The review highlights achievements in the application of 2D-COS by the beginning of September 2023.

中文翻译：

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二维相关光谱在化学分析中的应用

摘要

本文概述了广义二维相关光谱 (2D-COS) 在分析化学中的主要应用。 2D-COS 是一种用于分析光谱测量获得的数据集的方法。该方法基于使用二维相关图来检测和分析不同光谱区域或两种测量方法的数据之间的相关性。采用 2D-COS 的目的是通过发现数据中的潜在相关性来增加分析信息量。对一系列光谱数据的此类相关性进行分析，这些数据是在一系列分析物浓度、pH 值或混合物中的成分比例范围内以及在不同的温度或其他外部因素下获取的，从而能够研究和阐明化学过程和相互作用。该信息不容易直接从光谱中获得。与一维光谱相比，2D-COS 在分析信息的重要性方面表现出色，特别是在组分识别和复杂混合物的组成测定方面。 2D-COS 还可用于监测样本随时间的变化，这使其成为研究动态演化系统的宝贵工具。总体而言，2D-COS 是一种足够通用的方法，可以与多种方法结合使用，解决各种分析任务和复杂样品，而且无需样品制备。审查重点介绍了 2D-COS 应用在 2023 年 9 月之前取得的成就。