2D Correlation Spectroscopy and Its Application in Vibrational Spectroscopy Using Matlab (original) (raw)
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Techniques of two-dimensional (2D) correlation spectroscopy useful in life science research
Biomedical Spectroscopy and Imaging
Several illustrative examples are presented in this tutorial review to demonstrate the utility of 2D correlation spectroscopy, especially in life science applications. A set of IR spectra for a model protein system, which is undergoing complex thermally induced changes in the secondary structures, is analyzed by 2D correlation spectroscopy. The method of constructing 2D correlation maps from temperature-dependent IR spectra and their interpretation procedure are described. The resolution of overlapped protein IR bands by 2D correlation is demonstrated, and sequential order of spectral intensity changes is determined. Newly emerging techniques in the field, such as Pareto scaling, positive null-space projection, and 2D codistribution analysis, are discussed in some detail, in addition to the traditional application of 2D correlation spectroscopy.
Advances in two-dimensional correlation spectroscopy
Vibrational Spectroscopy, 2004
Current state of the perturbation-based generalized two-dimensional (2D) correlation spectroscopy is reviewed with the emphasis placed on the advances made since August 1999. This comprehensive review is the third of the consecutive series of reviews since 1993. Major reviews, tutorials, book chapters, a monograph and a journal special issue are discussed first. Recent fundamental and theoretical developments in 2D correlation spectroscopy, e.g., concept of synchronicity and linearity, new computational methods, display and visualization techniques, data normalization, reference spectrum selection, various raw data pre-treatment methods, and post-treatment of 2D spectra, data subdivision, simulation studies, statistical parameters relevant to 2D analysis, new or unusual forms of 2D correlation methods, sample-sample correlation and other chemometrics driven techniques are reviewed. The 2D spectroscopy based on various static perturbations, like temperature, concentration, pressure, position, and other physical variables, applied to different systems, including polymers, biomolecules, solutions, liquid crystals, etc., are examined. 2D correlation spectroscopy relying on transient phenomena, like chemical reactions, diffusion and other relaxation processes, are then studied. Dynamic 2D spectroscopy based on repetitive perturbations, e.g., mechanical, photoacoustic, or electrical stimuli, is also a very active area of research. A number of new types of probes and analytical methods are now incorporated into the field of perturbation-based 2D spectroscopy, including NMR, VCD, fluorescence, and even gel permeation chromatography (GPC) or molecular dynamics calculation. These techniques can be applied by themselves or further combined to provide new opportunities for various 2D hetero-correlation analyses.
Two-dimensional correlation spectroscopy — Biannual survey 2007–2009
Journal of Molecular Structure, 2010
The publication activities in the field of 2D correlation spectroscopy are surveyed with the emphasis on papers published during the last two years. Pertinent review articles and conference proceedings are discussed first, followed by the examination of noteworthy developments in the theory and applications of 2D correlation spectroscopy. Specific topics of interest include Pareto scaling, analysis of randomly sampled spectra, 2D analysis of data obtained under multiple perturbations, evolution of 2D spectra along additional variables, comparison and quantitative analysis of multiple 2D spectra, orthogonal sample design to eliminate interfering cross peaks, quadrature orthogonal signal correction and other data transformation techniques, data pretreatment methods, moving window analysis, extension of kernel and global phase angle analysis, covariance and correlation coefficient mapping, variant forms of sample-sample correlation, and different display methods. Various static and dynamic perturbation methods used in 2D correlation spectroscopy, e.g., temperature, composition, chemical reactions, H/D exchange, physical phenomena like sorption, diffusion and phase transitions, optical and biological processes, are reviewed. Analytical probes used in 2D correlation spectroscopy include IR, Raman, NIR, NMR, X-ray, mass spectrometry, chromatography, and others. Application areas of 2D correlation spectroscopy are diverse, encompassing synthetic and natural polymers, liquid crystals, proteins and peptides, biomaterials, pharmaceuticals, food and agricultural products, solutions, colloids, surfaces, and the like.
Recent advancement in the field of two-dimensional correlation spectroscopy
Journal of Molecular Structure, 2008
The recent advancement in the field of 2D correlation spectroscopy is reviewed with the emphasis on a number of papers published during the last two years. Topics covered by this comprehensive review include books, review articles, and noteworthy developments in the theory and applications of 2D correlation spectroscopy. New 2D correlation techniques are discussed, such as kernel analysis and augmented 2D correlation, model-based correlation, moving window analysis, global phase angle, covariance and correlation coefficient mapping, sample-sample correlation, hybrid and hetero correlation, pretreatment and transformation of data, and 2D correlation combined with other chemometrics techniques. Perturbation methods of both static (e.g., temperature, composition, pressure and stress, spatial distribution and orientation) and dynamic types (e.g., rheo-optical and acoustic, chemical reactions and kinetics, H/D exchange, sorption and diffusion) currently in use are examined. Analytical techniques most commonly employed in 2D correlation spectroscopy are IR, Raman, and NIR, but the growing use of other probes is also noted, including fluorescence, emission, Raman optical activity and vibrational circular dichroism, X-ray absorption and scattering, NMR, mass spectrometry, and even chromatography. The field of applications for 2D correlation spectroscopy is very diverse, encompassing synthetic polymers, liquid crystals, Langmuir-Blodgett films, proteins and peptides, natural polymers and biomaterials, pharmaceuticals, food and agricultural products, water, solutions, inorganic, organic, hybrid or composite materials, and many more.
Applied Spectroscopy, 2002
The direct combination of chemometrics and two-dimensional (2D) correlation spectroscopy is considered. The use of a reconstructed data matrix based on the significant scores and loading vectors obtained from the principal component analysis (PCA) of raw spectral data is proposed as a method to improve the data quality for 2D correlation analysis. The synthetic noisy spectra were analyzed to explore the novel possibility of the use of PCA-reconstructed spectra, which are highly noise suppressed. 2D correlation analysis of this reconstructed data matrix, instead of the raw data matrix, can significantly reduce the contribution of the noise component to the resulting 2D correlation spectra.
Applied Spectroscopy, 1993
A two-dimensional (2D) correlation method generally applicable to various types of spectroscopy, including IR and Raman spectroscopy, is introduced. In the proposed 2D correlation scheme, an external perturbation is applied to a system while being monitored by an electromagnetic probe. With the application of a correlation analysis to spectral intensity fluctuations induced by the perturbation, new types of spectra defined by two independent spectral variable axes are obtained. Such two-dimensional correlation spectra emphasize spectral features not readily observable in conventional one-dimensional spectra. While a similar 2D correlation formalism has already been developed in the past for analysis of simple sinusoidally varying IR signals, the newly proposed formalism is designed to handle signals fluctuating as an arbitrary function of time, or any other physical variable. This development makes the 2D correlation approach a universal spectroscopic tool, generally applicable to a very wide range of applications. The basic property of 2D correlation spectra obtained by the new method is described first, and several spectral data sets are analyzed by the proposed scheme to demonstrate the utility of generalized 2D correlation spectra. Potential applications of this 2D correlation approach are then explored.
Double two-dimensional correlation analysis – 2D correlation of 2D spectra
Journal of Molecular Structure, 2010
2D correlation analysis of 2D correlation spectra, i.e., double 2D correlation analysis, is carried out by a series of simple matrix multiplication operations. A new class of correlation spectra with higher selectivity and spectral resolution are generated. Quadrature 2D correlation enabled by the proper rescaling of double 2D correlation is especially useful for the analysis of the contravariant portion of spectral signals, which are 90 deg out of phase with the rest of data. The utility of double 2D correlation is most clearly demonstrated when it is applied to hetero-spectral or hetero-mode correlation analysis. Double hetero-correlation generates highly selective 2D spectra based on a portion of spectra which is projected onto a space spanned by other spectra or different spectral region. Illustrative examples are provided with Raman spectral monitoring of an emulsion polymerization process and a solution mixture undergoing compositional changes to show how double 2D correlation can be utilized.
Analytical Sciences, 2007
Generalized two-dimensional (2D) correlation analysis is a powerful and versatile tool applicable to the examination of data obtained from a very broad range of physical and analytical measurements, such as spectroscopy, chromatography, and microscopy. 1-6 In 2D correlation analysis, relationships among systematic variations in analytical signals, such as absorption intensities of spectra or elution intensities of chromatograms, are obtained as functions of two independent index variables, like spectroscopic frequencies or chromatographic retention times. The variations in analytical signals are induced by an external perturbation applied to the sample. The correlation intensities are displayed in the form of 2D maps for further analysis. These 2D maps are usually referred to as 2D correlation spectra, even though many of today's 2D correlation studies include applications in the field outside of spectroscopy. Figure 1 shows a typical example of a 2D correlation spectrum, displayed in the so-called fishnet plot or pseudo three-dimensional representation. In this spectrum, correlation intensities among IR absorptions of a polymer solution mixture undergoing compositional changes are plotted on a spectral plane defined by two independent IR wavenumber axes. Numerous peaks appearing over the 2D spectral plane provide surprisingly rich information about the dynamics of the compositional changes. More detailed discussion will be given later on this particular 2D correlation spectrum. The basic idea of applying a correlation method to generate such two-dimensional spectra was first introduced in the field of infrared (IR) spectroscopy. 7-10 The development of 2D IR correlation spectroscopy was motivated by the earlier introduction of 2D NMR spectroscopy, where the NMR spectral
Journal of Molecular Structure, 2006
Two-dimensional correlation spectroscopy (2D-COS) has been widely used to separate overlapped spectroscopic bands. However, band overlap may sometimes cause misleading results in the 2D-COS spectra, especially if one peak is embedded within another peak by the overlap. Based on the analysis of Lorentzian-type spectra, a new data normalization method has been proposed to overcome such overlap effect. The normalization factor is expressed as the square of the mean value divided by the peak value of each spectrum, F 2 mean =F max. This expression may be viewed as a modification to the mean normalization method originally put forward by Ozaki and co-workers. Computing simulation demonstrated that the new method works well with simulated dynamic spectra.