title: Concatenated two-dimensional correlation analysis: a new possibility for generalized two-dimensional correlation spectroscopy and its application to the examination of process reversibility.
abstract: We propose a new application of generalized two-dimensional (2D) correlation spectroscopy called "concatenated" 2D correlation analysis, which is useful in identifying the presence of strict similarity or very subtle difference between two spectral data sets having a similar origin. This approach is very efficient and can offer many potential applications. In this study, the detailed examination of process reversibility is explored. Two forms of concatenation, horizontal and vertical concatenation of data matrices, are introduced and the latter is discussed in detail. Concatenated 2D correlation analysis allows one to investigate directly the correlation between two independent but related spectral data sets. It can extract more detailed information, such as the comparison of effects of two different perturbations or different systems. We describe the principle of the "mirror-image concatenation" in 2D correlation analysis, which is applied to demonstrate its reliability and efficiency on three spectral models: a synthetic simulation data set; experimental Fourier transform infrared (FT-IR) spectra of the thermally induced unfolding-refolding transition of bovine pancreatic ribonuclease A (RNase A) in aqueous solution; and a set of FT-IR spectra of traditional Chinese medicines (TCM) of similar origin. The concatenated 2D correlation analysis shows its power in revealing the irreversibility of the thermally induced conformation transition of RNase A as well as the comparison of different species of TCM.
authors.affiliation: State Key Lab for Supramolecular Structure and Material, Jilin University, No. 2699, Qianjin Street, Changchun, 130012 P.R. China.
authors.forename: Liping
authors.initials: L
authors.lastname: Zhang
authors.forename: Isao
authors.initials: I
authors.lastname: Noda
authors.forename: Yuqing
authors.initials: Y
authors.lastname: Wu