Applications of moving window two-dimensional correlation spectroscopy to analysis of phase transitions and spectra classification

Our recently proposed idea of moving window two-dimensional (2D) correlation spectroscopy, which partitions a data set into series of relatively small submatrices (windows) and calculates their covariance maps in succession, is tested for three convoluted data set. Phase-transition temperatures of oleic acid and poly-(N-isopropylacrylamide) in an aqueous solution are sought by analyzing covariances of their temperature-dependent near-infrared and infrared spectra, respectively, while Raman spectra of three kinds of polyethylene (PE) pellets are investigated to find the spectral differences among them and to classify randomly ordered spectra by a sample-sample (SS) covariance map. The criterion of mean of standard deviation of covariance matrices is used as an indicator of the crucial information present in these matrices so that only a few of them are discussed in details. The results are obtained quickly after very simple calculations and are studied at length. The baseline variation is not removed prior to the calculations but is found to be of use for the determination of the phase-transition temperatures. Randomly ordered Raman spectra of the PE pellets are classified by innovatively used and interpreted SS slice spectra, with the relation to principal component analysis discussed.     

Statistical two-dimensional correlation spectroscopy: its theory and applications to sets of vibrational spectra

The present study aims at developing a new form of two-dimensional (2D) correlation spectroscopy, statistical 2D correlation spectroscopy. Statistical 2D spectroscopy differs from the widely used generalized 2D correlation spectroscopy in that the former abstracts spectral features by pretreatment and by 2D maps that are limited by the correlation coefficients in the range from 1 to -1. In this paper, the theory of the new 2D method is briefly described, and then its applications are discussed to reveal spectral and concentration features of artificial model spectra, infrared spectra of polycondensation of bis(hydroxyethyl terephthalate) measured on-line, and short-wave near-infrared spectra of raw milk. The results are analyzed thoroughly and compared with those from generalized 2D correlation spectroscopy and partial least-squares loadings and scores. The most significant advantage of statistical 2D correlation spectroscopy is that the 2D correlation spectra are easy to calculate and are purely mathematical in nature, thereby eliminating any subjective involvement of an experimenter, while the inherent weakness of the method lies in its sensitivity to the noise.     

Perturbation-correlation moving-window two-dimensional correlation spectroscopy

A new method of analysis, perturbation-correlation moving-window two-dimensional (PCMW2D) correlation spectroscopy, is proposed. For a spectral data set collected under an external perturbation, this method provides a pair of synchronous and asynchronous two-dimensional correlation spectra plotted on a plane between a spectral variable (e.g., wavenumber) axis and a perturbation variable (e.g., temperature) axis. One of the advantages of this new correlation analysis method is that complicated spectral variation along the perturbation direction can be monitored. It has been found that the synchronous and asynchronous PCMW2D correlation spectra are similar to the first perturbation derivative and negative second perturbation derivative spectra of the original data, respectively. To demonstrate the potential of PCMW2D correlation spectroscopy, it has been applied to temperature-dependent infrared (IR) spectra of a poly(vinyl alcohol) (PVA) film. The thermal behavior of the PVA film has been revealed by the PCMW2D correlation analysis. Two characteristic cross-peaks are observed in the synchronous PCMW2D correlation spectra generated from the temperature-dependent IR spectra between the crystalline phase C-O stretching band at 1141 cm-1 and the melting temperature of 209 degrees C and between the amorphous phase C-O stretching band at 1095 cm-1 and another specific temperature of 233 degrees C. This specific temperature of 233 degrees C corresponds to the thermal degradation temperature due to the elimination of the hydroxyl group attached to the main chain.     

A novel tool for two-dimensional correlation spectroscopy

The contour map of a correlation matrix of two different spectroscopies applied to the same samples is a useful interpretation tool to establish relations between the two spectroscopies. The problem is that these maps are generally very complex. This paper describes a method to resolve the correlation contour map into a few spectroscopically meaningful submaps.     

Quadrature orthogonal signal corrected two-dimensional correlation spectroscopy

Orthogonal signal correction (OSC) removes a substantial part of the spectral response that is orthogonal to the selected external variable. The combined use of OSC filtering and two-dimensional (2D) correlation analysis was proposed in the previous study (Wu, Noda, Meersman, and Ozaki, J. Mol. Struct., 2006, paper in press) to enable one to obtain high-quality 2D correlation spectra by eliminating any information unrelated to the external variables. However, the direct application of OSC to two-dimensional (2D) correlation analysis will result in the loss of the component that is significantly perpendicular to the external variable but also is the portion significant to the asynchronous 2D correlation analysis. Therefore, in order to avoid the problem of losing the valuable asynchronous 2D correlation information, the present study proposes a modified OSC filtering method, which is called quadrature OSC (QOSC) filtering. By replacing the external variable vector y used for OSC filtering with a two-column Y matrix consisting of y and its Hilbert-Noda transformation, the component of the spectral data asynchronously correlated to the external variable y is preserved. The performance of this technique on two simulated spectra data sets with a strong contaminant band and systematic noises has demonstrated that QOSC filtering 2D correlation analysis enables not only the elimination of the influence of signals that are unrelated to the external variable but also the preservation of the portions of information in the data matrix that are 90 degrees out of phase with y. It enables OSC 2D to deal with the problems of losing the portion of information that is perpendicular to the external variable y but is quite significant to the 2D correlation analysis.     

Synthetic spectra: a tool for correlation spectroscopy

We show that computer-generated diffractive optical elements can be used to synthesize the infrared spectra of important compounds, and we describe a modified phase-retrieval algorithm useful for the design of elements of this type. In particular, we present the results of calculations of diffractive elements that are capable of synthesizing portions of the infrared spectra of gaseous hydrogen fluoride (HF) and trichloroethylene (TCE). Further, we propose a new type of correlation spectrometer that uses these diffractive elements rather than reference cells for the production of reference spectra. Storage of a large number of diffractive elements, each producing a synthetic spectrum corresponding to a different target compound, in compact-disk-like format will allow a spectrometer of this type to rapidly determine the composition of unknown samples. Other advantages of the proposed correlation spectrometer are also discussed.     

"Sequential order" rules in generalized two-dimensional correlation spectroscopy

The widely used "sequential order" rules in the generalized two-dimensional (2D) correlation spectroscopy were adopted from the mechanical perturbation-based 2D infrared, where dynamic spectral intensity variation must be a simple sinusoid. 2D correlation analysis is fundamentally a form of parametrization of the integrated or overall relationship between two variable quantities. In generalized 2D correlation spectroscopy, however, the dynamic spectral intensity variations are generally nonperiodic and monotonic, and spectral intensity changes are largely instantaneous. The sequential orders in generalized situations are therefore localized. It is naturally necessary and important to testify whether the analysis result obtained by using the sequential order rules is consistent with the local sequential order of events, which reflects the real sequential order in generalized situations. Unfortunately, this test was not done yet. In this report, the sequential order rules have been tested in the generalized situations using simulated spectra with different local sequential orders and assuming the intensity changes of bands take the exponential forms. It has been found that the sequential order rules correctly identify the local sequential order of two events when spectral intensities of two bands increase or decrease at the same direction but fail when spectral intensities change at different directions. In addition, 2D correlation analysis cannot distinguish the local sequential order from the rate difference of events. A theoretical analysis demonstrates that the synchronous and asynchronous spectra in the generalized 2D correlation spectroscopy may also indicate the linear/nonlinear relationship, in addition to the integrated or overall sequential order of events. The synchronous and asynchronous spectra in the generalized 2D correlation spectroscopy do not necessarily provide the information on the local sequential order or rate difference of events.     

Structure analysis of poly(N-isopropylacrylamide) using near-infrared spectroscopy and generalized two-dimensional correlation infrared spectroscopy

Based on a detailed study of the fundamental vibrations in the mid-infrared (MIR) region and supported by NH-proton deuteration results, the assignment of the overtone and combination bands in the near-infrared (NIR) spectrum of poly(N-isopropylacrylamide) (PNIPAM) is presented. Variable-temperature experiments and two-dimensional correlation infrared spectroscopy are used to determine the chemical mechanism and changing sequence of groups in PNIPAM; we conclude that bonded NH groups turn into free NH groups during the heating process, while the CH groups on the side-chains change prior to those on the main chains. A heterospectral dynamical correlation between the NIR and MIR regions or H-included groups in both regions was also performed. The temperature-induced dissociation of the hydrogen-bonded NH groups is found to proceed earlier than the conformational changes in the hydrocarbon chains.     

Revealing system dynamics through decomposition of the perturbation domain in two-dimensional correlation spectroscopy

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.     

Protein structure determination in solution by two-dimensional and three-dimensional nuclear magnetic resonance spectroscopy

Over the last decade, nuclear magnetic resonance (NMR) spectroscopy has evolved into a powerful method for determining structures of biological macromolecules. This has opened a unique opportunity for obtaining high-resolution three-dimensional structures in solution, in contrast to the well-established methods of X-ray diffraction, which are applicable only to solids and in particular single crystals. This rapid development has been spurred by several key advances in the field, especially the introduction of two- and three-dimensional NMR experiments, high field spectrometers (500 and 600 MHz), and computational algorithms for converting NMR derived restraints into three-dimensional structures. This review outlines the methodology employed for solving protein structures in solution, describing the basic NMR experiments necessary as well as introducing the concepts upon which the computational algorithms are founded. A variety of examples is discussed, illustrating the present state of the art, and future possibilities are indicated.     

Two-dimensional correlation spectroscopy: effect of reference spectrum on noise-free and noisy spectra

It has been shown that for two-dimensional (2D) correlation analysis with a perturbation average, the first or the last spectrum in the data set as a reference provides identical qualitative results. On the other hand, selection of the reference spectrum significantly different from the spectra used for 2D correlation analysis may complicate interpretation of the contour plots and in consequence lead to erroneous results. The effect of noise is relatively small when 2D correlation spectra are calculated without the reference spectrum. For the other reference spectra the magnitude of the noise effect is comparable. In all cases, the asynchronous spectra are more strongly affected by the noise as compared to the synchronous spectra.     

Chi-squared-based filters for high-fidelity signal-to-noise ratio enhancement of spectra

When reconstructing a measured spectrum to enhance its signal-to-noise ratio (SNR), the objective is to minimize the variance between the smooth reconstructed spectrum and the original measured spectrum, hence to attain an acceptably small chi2 value. The chi2 value thus measures the fidelity of the reconstruction to the original. Smoothness can be conceived as attenuated variation between adjacent points in a spectrum. Thus, a conceptual change in the application of the chi2 function to the difference between adjacent points of the reconstructed spectrum permits its use, in principle, as both a measure of smoothness and a measure of fidelity. We show here that implementations of this concept produce results superior to Savitzky-Golay filters.     

采用二维红外技术研究交联对碱溶胶原结构的影响

以N-羟基琥珀酰亚胺己二酸酯(NHS-AA)为交联剂,交联改性碱溶胶原,采用二维红外相关光谱法研究了交联对胶原二级结构的影响。研究发现,交联未影响胶原红外特征吸收峰的位置,但1672、1554和1241cm-1归属于胶原酰胺I带的CO伸缩振动、酰胺Ⅱ带的C—N伸缩与N—H弯曲振动和Ⅲ带的N—H面内变形振动峰之间存在同步正交叉峰,表明随交联共价键的增加,胶原的链段构象发生了变化。在NHS-AA用量增加的过程中,胶原二级结构变化的顺序为:酰胺Ⅲ带>酰胺Ⅰ带>酰胺Ⅱ带>—CH3>—CH—。由此可见,二维红外相关分析法能提供由交联引起的胶原构象动态变化的微观信息,为进一步研究改性胶原结构与功能之间的关系提供实验依据。     

Identification and interpretation of generalized two-dimensional correlation spectroscopy features through decomposition of the perturbation domain

Generalized two-dimensional correlation spectroscopy offers great scope for revealing the behavior of relationships between components of a system under empirical study. We have developed methods that aid in the interpretation of two-dimensional correlation spectroscopy. These methods include reference patterns for two-dimensional correlation and correlation coefficient maps, their superposition and joint interpretation, and the use of delta functions to decompose them in the perturbation domain. We show how their joint use permits discrimination between similar two-dimensional correlation map features on the basis of different correlation coefficients. We also show how the decomposition of maps into the perturbation domain reflects the dynamic behavior of spectral features over the course of the perturbation and permits discrimination between otherwise highly similar two-dimensional correlation cross-peaks. These approaches simplify the interpretation of two-dimensional correlation spectroscopy maps and facilitate access to their rich information content.     

Statistical and generalized two-dimensional correlation spectroscopy of multiple ionization States. Fluorescence of neurotransmitter serotonin

Fluorescence spectra of neurotransmitter serotonin are analyzed with generalized and statistical two-dimensional correlation spectroscopy. A comparison is provided for these two emerging data analysis techniques. Both methods reveal correlations between spectral variables and demonstrate enhanced sensitivity in detecting the dynamic spectral changes over conventional one-dimensional spectroscopy. Both statistical and generalized 2D correlation analysis emphasize simultaneous spectral changes in response to external perturbations. Generalized 2D correlation spectroscopy further reveals the difference in rates of these dynamic changes. Using 2D correlation analysis, a third ionization species of serotonin is identified using pH and excitation wavelength perturbation. This species is a doubly deprotonated serotonin with very low fluorescence quantum yield, confirmed by using a laser excitation at longer wavelength and at higher pH. Taking advantage of the spectral differences between excitation of serotonin and tryptophan, as low as 3.8 nM serotonin can be detected in the presence of 20 microM tryptophan, with long-wavelength excitation. This represents the sensitive detection of serotonin in 5000-fold excess of tryptophan.     

A study on water adsorption onto microcrystalline cellulose by near-infrared spectroscopy with two-dimensional correlation spectroscopy and principal component analysis

Water adsorption onto microcrystalline cellulose (MCC) in the moisture content (M(c)) range of 0.2-13.4 wt % was investigated by near-infrared (NIR) spectroscopy. In order to distinguish heavily overlapping O-H stretching bands in the NIR region due to MCC and water, principal component analysis (PCA) and generalized two-dimensional correlation spectroscopy (2DCOS) were applied to the obtained spectra. The NIR spectra in four adsorption stages separated by PCA were analyzed by 2DCOS. For the low M(c) range of 0.2-3.1 wt %, a decrease in the free or weakly hydrogen-bonded (H-bonded) MCC OH band, increases in the H-bonded MCC OH bands, and increases in the adsorbed water OH bands are observed. These results suggest that the inter- and intrachain H-bonds of MCC are formed by monomeric water molecule adsorption. In the M(c) range of 3.8-7.1 wt %, spectral changes in the NIR spectra reveal that the aggregation of water molecules starts at the surface of MCC. For the high M(c) range of 8.1-13.4 wt %, the NIR results suggest that the formation of bulk water occurs. It is revealed from the present study that approximately 3-7 wt % of adsorbed water is responsible for the stabilization of the H-bond network in MCC at the cellulose-water surface.     

Studies on the structure of water using two-dimensional near-infrared correlation spectroscopy and principal component analysis

The structure of water molecules in the pure liquid state has been subjected to extensive research for several decades. Questions still remain unanswered, however, and no single model has been found capable of explaining all the anomalies of water. In the present study, near-infrared spectra of water in the temperature region 6-80 degrees C have been analyzed by use of principal component analysis and two-dimensional correlation spectroscopy in order to study the dynamic behavior of a band centered around 1,450 nm at room temperature, which is due to the combination of symmetric and antisymmetric O-H stretching modes (first overtone) of water. It has been found that the wavelengths 1,412 and 1,491 nm account for more than 99% of the spectral variation, representing two major water species with weaker and stronger hydrogen bonds, respectively. A third species located at 1438 nm, whose concentration was relatively constant as a function of temperature, is also indicated. A somewhat distorted two-state structural model for water is suggested.