Novel Use of a Static Modification of Two-Dimensional Correlation Analysis. Part I: Comparison of the Secondary Structure Sensitivity of Electronic Circular Dichroism, FT-IR, and Raman Spectra of Proteins

Two-dimensional (2D) correlation coefficient analysis is employed to classify and characterize spectral variations among heavily overlapped near-infrared spectra of pellets and films of three kinds of polyethylene (PE), high-density (HD), low density (LD), and linear low-density (LLD) polyethylene, and five kinds of ivory signature seals. The sample-sample (SS) 2D correlation maps are used for classification while the wavenumber-wavenumber (WW) 2D correlation maps are used for determining spectral variation among the above materials. Both correlation maps are obtained by multiplying the original data with themselves. It is found that the NIR spectra of pellets and films of HD PE are clearly different from those of LD PE and LLD PE, while the NIR spectra of five kinds of ivory seals yield easily discernable squares in the SS correlation maps. The background variation is thought to be behind the differentiation of the PE samples because the WW correlation maps do not indicate appearance of new bands. The correlation results are compared with those of principal component analysis (PCA). This study is a novel application of 2D correlation coefficient analysis which reveals that a comprehensive description of demanding spectral systems is achievable by utterly simple mathematical means because 2D correlation maps are obtained via a single mathematical operation.

The relative error caused by reproducibility in two-dimensional correlation spectroscopy

Two-dimensional (2D) correlation analysis has been used in this study to identify changes in complex nuclear magnetic resonance (NMR) metabonomics spectra of rat urine samples obtained during a study in which vasculitis (vascular injury), an important safety element in preclinical trials, was induced. Two types of correlation analysis were performed, along the variables and along the samples, and both 2D covariance and correlation coefficient maps were calculated. The binned and ''raw'' NMR spectra were analyzed (0.04 and 0.001 ppm resolution, respectively). Good correlation was found among the major peaks of the binned spectra, and two groups of samples were identified using sample-sample 2D correlation maps. Much more complex correlation features were obtained from the ''raw'' spectra, in which the specific, butterfly-like patterns were obtained in the covariance map but with only a few significant correlation coefficients in the corresponding 2D correlation maps. In terms of classification, the same group of the last nine spectra that indicated the end of the process and clustered in the 2D sample-sample covariance map of the binned data was also found in the 2D sample-sample covariance map of the raw NMR spectra but, again, not in the 2D correlation coefficient map. A discussion is given on the details of the application of the correlation analysis with regard to spectral data resolution, alignment, the effect of actual intensities of the NMR signal, and reference to various results from 2D correlation analysis of vibrational spectra.

The three-step polymerization process of a well-defined polymeric silane coupling agent, 3-(triethoxysilyl)propyl-terminated polystyrene, catalyzed by 1 mol/kg KOH–H2O, was traced as a function of reaction time using a gel-permeation chromatography (GPC) technique. A set of GPC traces collected during the polymerization was then converted to two-dimensional (2D) correlation maps, by using generalized 2D correlation analysis. The 2D correlation GPC maps reflected the details of the aggregational behavior (in particular, the aggregate–aggregate correlations) in each step of the polymerization, thus demonstrating the promising potential of this technique.

Protein Secondary Structure from Fourier Transform Infrared and/or Circular Dichroism Spectra

Short-wave near-infrared (NIR) spectra of raw milk have been analyzed in the 800–1100 nm region by two-dimensional (2D) correlation spectroscopy. In this study, we have used both the well-known generalized 2D correlation spectroscopy method, which yields correlation coefficients among spectral variances on all the wavelength points (wavelength-wavelength correlation), and a novel sample-sample correlation spectroscopy method, which gives correlation coefficients among the concentration dynamics of the species in the system. The sample-sample correlation spectroscopy develops correlation maps with the samples on the axes. First, a set of 34 spectra was ordered according to increasing fat content, while all other milk components varied freely. Both synchronous and asynchronous wavelength-wavelength correlation maps have shown strong baseline changes despite the use of multiplicative scatter correction as a pretreatment. Bands at 930 and 970 nm due to fat and water, respectively, have been found to be the most significant spectral features. The synchronous sample-sample correlation map was calculated from the original data matrix and was compared with the outer product of the fat concentration vector. The comparison has revealed that the main spectral variances in the raw milk spectra in the short-wave NIR region are due to fat. The same procedure was repeated for a set of 15 samples that contained a constant fat content and were ordered according to increasing protein content. Poor agreement was found between the outer product of the protein concentration vector and the synchronous sample-sample correlation map. This result suggests that the spectral variances in raw milk spectra that have a constant fat content are due not exclusively to proteins but also due to other milk components. Comparisons of partial least-squares (PLS) regression analysis of fat and proteins and both sample-sample and wavelength-wavelength 2D correlation analyses of raw milk spectra have been made.

A technique is presented to simply and effectively decompose the perturbation domain in two-dimensional (2D) correlation maps calculated on a given set of vibrational spectra. Decomposition of the perturbation domain exposes a wealth of kinetic information complementary to the information extracted from conventional 2D correlation spectroscopy. It is shown that the technique produces "perturbation profile maps" that can be utilized in both the interpretation of the conventional 2D correlation maps and the independent kinetic analysis of the given system. Discrimination between spectral features exhibiting similar, but not identical, dynamics is facilitated by the decomposition, and spectral features exhibiting identical dynamics over the perturbation interval are quickly identified. Spectral features exhibiting similar dynamics over only a sub-range of the full perturbation are also identifiable. Interpretation of phase information illuminated in synchronous and asynchronous maps is simplified. Comparison between similar spectral features present in different samples is facilitated with the technique. The simplicity and ease of implementation of the technique make decomposition of the perturbation domain a valuable addition to the tools available in 2D correlation analysis.

Mapping capability of linear correlation statistics for characterization of complex materials using laser-induced breakdown spectroscopy

Two-dimensional infrared (2D-IR) cross-correlation can facilitate the interpretation of infrared spectra. However, the fluctuation of noise and the overlapped component bands often complicate the analysis of 2D-IR spectra. The ratio of asynchronous to synchronous correlation functions ( F) can serve as a control of coherence for the cross peaks in 2D correlation maps. A synchronous cross peak with an F value close to 0 corresponds to the good coherence of cross-correlated pair of signals, more likely reflecting a true synchronous cross peak, while a large F value associated with a synchronous cross peak is more likely a false cross peak. Similarly, the noncoherence of asynchronous cross peaks can be checked by computing their F values. In this case, large F values are associated with true asynchronous correlation peaks, while small F values are more likely false asynchronous cross peaks. This approach has been tested on smoothed and unsmoothed near-infrared (NIR) spectra of human serum albumin (HSA) in aqueous solutions. A set of NIR spectra of HSA with increasing concentration were measured to generate 2D correlation maps. Unsmoothed spectra permitted the acquisition of synchronous and asynchronous 2D correlation maps containing all putative cross peaks in addition to artifacts generating by the fluctuation of noise or band overlapping. The control of coherence of pairs of signals on each cross peak, based on their F values, facilitated the interpretation of 2D-NIR maps, by improving the selection of true cross peaks from false cross peaks. The smoothed NIR spectra provided cross peaks without any artifacts in the synchronous and asynchronous correlation maps. The selection of cross peaks based on their F values confirmed the absence of false cross peaks in the case of smoothed spectra. Despite some limitations as discussed in this report, this procedure is straightforward to implement and will make the analysis of 2D vibrational spectra much easier.

Human erythrocytes analyzed by generalized 2D Raman correlation spectroscopy

In this study, a silk fiber/graphene oxide (GO) composite was directly obtained by feeding silkworm larvae with GO. Methods such as temperature-dependent Fourier transform infrared (FT-IR) spectroscopy, second-derivative analysis and two-dimensional (2D) correlation FT-IR spectroscopy were used to investigate the bonding interactions between GO and silk fibers in the silk fiber/GO composite. In addition, the silk fiber/GO composite was physically characterized by scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). Temperature-dependent second-derivative FT-IR spectra of the reinforced silk fiber/GO composite showed a characteristic absorption band of GO at 1053 cm-1 and 40 °C, confirming the incorporation of GO into the silk fiber matrix. 2D correlation analysis of the temperature-dependent FT-IR spectra indicated that the asynchronous correlation intensity of the silk fiber/GO composite decreased 1.7-fold compared with that of the silk fiber, and the number of correlation peaks was significantly reduced, which suggests that GO present in the silk fiber/GO composite is thermally reduced with increasing temperature, resulting in enhanced thermally stability. The results of TGA showed that the silk fiber/GO composite exhibits higher decomposition temperatures than the silk fiber, which is in good agreement with the temperature-dependent 2D correlation analysis.

We study ionization and fragmentation of tetrafluoromethane (CF4) molecule induced by electron impact at low energies (E0 = 38 and 67 eV). We use a reaction microscope combined with a pulsed photoemission electron beam for our experimental investigation. The momentum vectors of the two outgoing electrons (energies E1, E2) and one fragment ion are detected in triple coincidence (e, 2e+ ion). After dissociation, the fragment products observed are CF3+, CF2+, CF+, F+ and C+. For CF3+ and CF2+ channels, we measure the ionized orbitals binding energies, the kinetic energy (KE) of the charged fragments and the two-dimensional (2D) correlation map between binding energy (BE) and KE of the fragments. From the BE and KE spectra, we conclude which molecular orbitals contribute to particular fragmentation channels of CF4. We also measure the total ionization cross section for the formation of CF3+ and CF2+ ions as function of projectile energy. We compare our results with earlier experiments and calculations for electron-impact and photoionization. The major contribution to CF3+ formation originates from ionization of the 4t2 orbital while CF2+ is mainly formed after 3t2 orbital ionization. We also observe a weak contribution of the (4a1)−1 state for the channel CF3+.Graphical abstract

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.

Predictions of Secondary Structure using Statistical Analyses of Electronic and Vibrational Circular Dichroism and Fourier Transform Infrared Spectra of Proteins in H 2O

Potentials of variable–variable and sample–sample, generalized and statistical, two-dimensional correlation spectroscopies in investigations of chemical reactions