Two-dimensional correlation analysis for a kinetic model of consecutive reactions

We have systematically studied a kinetic scheme of consecutive reactions by using generalized two-dimensional (2D) correlation analysis as reported by Noda. The correlations between reactant-intermediate, intermediate-product, and reactant-product pairs are analyzed with the related rate constants and absorption coefficients. When the reference spectrum is set at zero, the synchronous and asynchronous correlation spectra for the kinetic model are almost free from the influence of poor quality signals. If an appropriate reference spectrum is selected, the intermediate can be feasibly distinguished from other species involved in the reaction. A ratio of asynchronous to synchronous correlation intensity yields a coherence spectrum, which is characterized by different plateau-type peak intensities. When a steady-state condition fits the kinetic model, the peak summation of reactant-intermediate and intermediate-product correlation will approach the intensity of the reactant-product correlation. The coherence spectrum is useful for pattern recognition of the reaction scheme and also provides an effective way to identify the location and the extent of spectral overlap between two peaks.     

Two-dimensional correlation analysis in application to a kinetic model of parallel reactions

By applying generalized two-dimensional (2D) correlation analysis as reported by Noda, we have systematically studied a kinetic model of parallel reactions. Given the related rate constants and absorption coefficients, the correlation between reactant and products are analyzed. The reactant-reactant, reactant-product, and product-product pairs are found to be synchronously correlated, and their intensities increase with increase of the rate constant and the absorption coefficient. On the other hand, only the reactant-product pairs show in the asynchronous spectra. Their intensities also depend proportionally on the rate constant and the absorption coefficient. The influence of signal-to-noise ratio (S/N) and overlapped spectra are further discussed. The resulting synchronous and asynchronous correlation spectra for the kinetic model appear to be weakly influenced by poor quality of the signal when the reference spectrum is set at zero. The ratio of asynchronous to synchronous correlation intensity yields a coherence spectrum. This spectrum remains a constant intensity for all the correlated peaks, being free from the influence of rate constant and absorption coefficient as well as being weakly disturbed by a small S/N ratio. It also provides a way to evaluate the extent of spectral overlap between two peaks. The coherence spectrum is useful to characterize the type of parallel reactions.     

New approach to avoid erroneous interpretation of results derived from generalized two-dimensional correlation analysis for applications in catalysis

Surface characterization and catalysis can significantly benefit from the application of generalized two-dimensional (2D) correlation analysis. This two-dimensional approach allows a better resolution of overlapping peaks, can reveal new features not readily observable in the raw spectra, gives clear evidence for spectral intensities that change as an effect of a perturbation applied to the system, and allows the establishment of time sequences for the changes occurring in different spectral features of interest for determining reaction intermediates and/or mechanisms. The interpretation of the synchronous and asynchronous plots was observed to lead to erroneous time sequences when spectral features change in a non-monotonic way, such as a biphasic or oscillatory behavior, under the influence of a perturbation. We propose a new approach to the 2D correlation analysis to avoid misinterpretation of the results calculated in the asynchronous plot. Progressive correlation analysis (ProCorA) calculates the synchronous plot from the first two spectra of the data matrix and one spectrum is added at every step of the analysis. The sequence of changes can be set up from the progressive evolution of peaks in both the synchronous and asynchronous plots.     

Investigation of polypeptide conformational transitions with two-dimensional Raman optical activity correlation analysis, applying autocorrelation and moving window approaches

The study of conformational transitions in polypeptides is not only important for the understanding of folding mechanisms responsible for the self-assembly of proteins but also for the investigation of the misfolding of proteins that can result in diseases including cystic fibrosis, Alzheimer's, and Parkinson's diseases. Our recent studies developing two-dimensional Raman optical activity (ROA) correlation analysis have proven to be successful in the investigation of polypeptide conformational transitions. However, the complexity of the ROA spectra, and the 2D correlation synchronous and asynchronous plots, makes data analysis detailed and complex, requiring great care in interpretation of 2D correlation rules. By utilizing the 2D correlation approaches of autocorrelation and moving windows it has been possible to gain further information from the ROA spectral data sets in a simpler and more consistent way. The most significant spectral intensity changes have been easily identified, facilitating appropriate interpretation of synchronous plots, and transition phases have been identified in the moving window plots, directly relating spectral intensity changes to the perturbation.     

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.     

Application of two-dimensional correlation spectroscopy to chemometrics: self-modeling curve resolution analysis of spectral data sets

This paper demonstrates the use of two-dimensional (2D) correlation spectroscopy in conjunction with alternating least squares (ALS) based self-modeling curve resolution (SMCR) analysis of spectral data sets. This iterative regression technique utilizes the non-negativity constraints for spectral intensity and concentration. ALS-based SMCR analysis assisted with 2D correlation was applied to Fourier transform infrared (FT-IR) spectra of a polystyrene/methyl ethyl ketone/deuterated toluene (PS/MEK/d-toluene) solution mixture during the solvent evaporation process to obtain the pure component spectra and then the time-dependent concentration profiles of these three components during the evaporation process. We focus the use of asynchronous 2D correlation peaks for the identification of pure variables needed for the initial estimates of the ALS process. Choosing the most distinct bands via the positions of asynchronous 2D peaks is a viable starting point for ALS iteration. Once the pure variables are selected, ALS regression can be used to obtain the concentration profiles and pure component spectra. The obtained pure component spectra of MEK, d-toluene, and PS matched well with known spectra. The concentration profiles for components looked reasonable.     

New approach to generalized two-dimensional correlation spectroscopy. III: Eigenvalue manipulation transformation (EMT) for spectral selectivity enhancement

This paper demonstrates the potential of eigenvalue manipulating transformation (EMT) of a data matrix for spectral selectivity enhancement, especially useful in 2D correlation analysis. The EMT operation aims at the accentuation of select features of the information content of the original data matrix. For example, by uniformly lowering the power of a set of eigenvalues associated with the original data, the smaller eigenvalues become more prominent and the contributions of secondary loadings become amplified. As a direct consequence of the minor factor accentuation by such EMT operations, 2D correlation spectra gain much stronger discriminating power. The selectivity enhancement effect of such manipulation of eigenvalues is much more noticeable on the synchronous 2D correlation spectrum. This improvement for the spectral selectivity of synchronous 2D correlation spectra is potentially very important, as we usually put more emphasis on the interpretation of asynchronous 2D spectra in 2D correlation analysis due to overlaps of synchronous peaks. Such EMT operations tend to exaggerate the information content of minor PCs and reduce that of major PCs. Thus, much more subtle difference of spectral behavior for each component is now highlighted. Surprisingly, asynchronous 2D correlation spectra are found to be much less sensitive to such EMT operations. The result indicates that the distinction of different band responses has already been accomplished effectively by the original asynchronous 2D correlation analysis.     

On the use of band-target entropy minimization to simplify the interpretation of two-dimensional correlation spectroscopy

Two-dimensional (2D) correlation spectroscopy has been extensively applied to analyze various vibrational spectroscopic data, especially infrared and Raman. However, when it is applied to real-world experimental data, which often contains various imperfections (such as noise interference, baseline fluctuations, and band-shifting) and highly overlapping bands, many artifacts and misleading features in synchronous and asynchronous maps will emerge, and this will lead to difficulties with interpretation. Therefore, an approach that counters many artifacts and therefore leads to simplified interpretation of 2D correlation analysis is certainly useful. In the present contribution, band-target entropy minimization (BTEM) is employed as a spectral pretreatment to handle many of the artifact problems before the application of 2D correlation analysis. BTEM is employed to elucidate the pure component spectra of mixtures and their corresponding concentration profiles. Two alternate forms of analysis result. In the first, the normally vxv problem is converted to an equivalent nvxnv problem, where n represents the number of species present. In the second, the pure component spectra are transformed into simple distributions, and an equivalent and less computationally intensive nv'xnv' problem results (v'相似文献   

Two-dimensional attenuated total reflection infrared correlation spectroscopy study of the desorption process of water-soaked cotton fibers

Two-dimensional (2D) correlation analysis was applied to characterize the attenuated total reflection (ATR) spectral intensity fluctuations of native cotton fibers with various water contents. Prior to 2D analysis, the spectra were leveled to zero at the peak intensity of 1800 cm(-1) and then were normalized at the peak intensity of 660 cm(-1) to subjectively correct the changes resulting from water diffusion in fibers and resultant density dilution. Next, a new spectral set was subjected to principal component analysis (PCA) and two clusters of hydrated (≥13.3%) and dehydrated (<13.3%) fibers were obtained. Synchronous and asynchronous 2D correlation spectra from individual ATR spectral sets enhanced spectral resolution and provided insights about water-content-dependent intensity variations not readily accessible from one-dimensional ATR spectra. The 2D results revealed remarkable differences corresponding to water loss between the hydrated and dehydrated fibers. Of interest were that: (1) the intensity of the 1640 cm(-1) water band remains in a steady state for hydrated fibers but decreases for dehydrated fibers; (2) during the desorption process of adsorbed water, small and water-soluble carbonyl species (i.e., esters, acids, carboxylates, and proteins) begin to accumulate on the cotton surface, resulting in possible changes in the coloration and surface chemistry of native cotton fibers that were rained on prior to harvesting; (3) intensities of bands in the 1200 to 950 cm(-1) region exhibit a more apparent intensity increase than those in the 1500 to 1200 cm(-1) region, indicating the sensitivity of the 1200 to 950 cm(-1) infrared (IR) region to intra- and inter-molecular hydrogen bonding in fiber celluloses; and (4) the 750 cm(-1) band, ascribed to the unstable I(α) phase in amorphous regions, might originate from the cellulose-water complex through hydrogen bonding.     

Spectral insight into intensity variations in phase-transition processes using two-dimensional correlation analysis

Two-dimensional correlation spectroscopy (2D-COS) is widely used in studies of phase-transition processes to provide valuable order information that is useful in investigating these mechanisms. During phase-transition processes, the spectral intensity always changes in an "S"- or "anti-S"-shaped curve. Sample selection of the large and complicated dataset for 2D correlation analysis may impact the resulting sequence and should be given serious consideration. Additionally, the relationship between the sequential order obtained from 2D-COS and the parameters of the intensity change, namely, the transition point and the change rate, is still poorly defined. This article makes an attempt to resolve these problems based on the analysis of simulated spectra by assuming that the band intensity changes in a sigmoid manner without a step delay. It is concluded that the sample range around a transition point with a drastic intensity change defined by asynchronous perturbation-correlation moving-window (PCMW2D) analysis is a reasonable choice, and in this region a band that changes earlier as determined by 2D-COS most likely has an earlier phase-transition point. Also, from the results of segmental analysis, it is proposed that 2D-COS can distinguish the sequence of two bands using only rate difference; however, the rate difference and the form of the intensity change should be considered comprehensively. The insights from the simulated results are applied to analyze the temperature-dependent infrared (IR) spectra of poly[di(butyl)vinyl terephthalate] (PDBVT). The phase-transition mechanism of PDBVT can be clearly found using a suitable sample selection method.     

On the normalization method in two-dimensional correlation spectra when concentration is used as a perturbation parameter

Data pretreatment is of importance in two-dimensional (2D) correlation analysis when composition is used as a perturbation parameter. For composition-oriented studies, different normalization methods based on both external parameters (i.e., concentration) and internal parameters (i.e., absorbance from individual components) have been compared. It was found that when there is no overlapping between absorption bands of interest, no normalization is needed for data pretreatment. When overlapped bands must be used for 2D correlation analysis, the mean-centered normalization method could be used to obtain correct signs in synchronous spectra for a transformation process in the specific form of A-->kC. The intensity of the 2D spectrum, however, may not accurately reflect quantitative information of the overall extent of spectral intensity variation observed during experiments.     

Two-dimensional fluorescence correlation spectroscopy with modulated excitation

Overlap of multiple states or multiple species in a chemical system often creates a congested fluorescence spectrum that is difficult to interpret. The resolution of component spectra is essential for the understanding of the structure and dynamics of such multicomponent systems. In this paper, two-dimensional fluorescence correlation spectroscopy (2D FCS) is presented for the dissection of component spectra using the time correlation function. In 2D FCS, the time response of fluorescence intensity is collected at various wavelengths upon an external perturbation. The time correlation function is evaluated between wavelengths. A two-dimensional fluorescence correlation spectrum, or a plot of the correlation intensity as a function of two wavelength axes, resolves the overall spectrum into component spectra. The characteristics of the two-dimensional time correlation function are demonstrated in the frequency domain fluorescence spectroscopy in which the sinusoidally modulated excitation provides the external perturbation. Using 2D FCS, fine vibronic structures of the component fluorescence emission spectra were completely resolved from a strongly overlapped one-dimensional mixture spectrum. The existence of multiple microenvironments of a probe molecule in a biological system is evidenced by nonzero asynchronous correlation intensities. The corresponding spectra are retrieved from correlation analysis. Unlike traditional resolution methods in fluorescence spectroscopy based on statistical fitting of fluorescence decays, 2D FCS can resolve species whose fluorescence decays are linked by the rate constants in chemical reactions and species displaying multiexponential decay kinetics.     

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.     

Characterization of attenuated total reflection infrared spectral intensity variations of immature and mature cotton fibers by two-dimensional correlation analysis

Two-dimensional (2D) correlation analysis was applied to characterize the attenuated total reflection (ATR) spectral intensity fluctuations of immature and mature cotton fibers. Prior to 2D analysis, the spectra were leveled to zero at the peak intensity of 1800 cm(-1) and then were normalized at the peak intensity of 660 cm(-1) to subjectively correct the variations resulting from ATR sampling. Next, normalized spectra were subjected to principal component analysis (PCA), and two clusters of immature and mature fibers were confirmed on the basis of the first principal component (PC1) negative and positive scores, respectively. The normalized spectra clearly demonstrated the intensity increase or decrease of the bands ascribed to different C-O confirmations of primary alcohols in the 1050-950 cm(-1) region, which was not apparent from raw ATR spectra. The PC1 increasing-induced 2D correlation analysis revealed remarkable differences between the immature and mature fibers. Of interest were that: (1) Both intensity increase of two bands at 968 and 956 cm(-1) and the shifting of 968 cm(-1) in immature fibers to 956 cm(-1) in mature fibers, together with the intensity decreasing and shifting of the 1048 and 1042 cm(-1) bands, are the characteristics of cotton fiber development and maturation. (2) Intensities of most bands in the 1800-1200 cm(-1) region decreased with the fiber growth, suggesting they are from either noncellulosic components or CH and OH fractions in amorphous celluloses. (3) The reverse sequence of intensity variations of the bands in the 1100-1000 cm(-1) and 1000-900 cm(-1) region of asynchronous spectra indicated a different mechanism of compositional and structural changes in developing cotton fibers at different growth stages.     

Two-dimensional correlation study of uniaxially drawn poly(ethylene terephthalate) films by using attenuated total reflection based dynamic compression modulation step-scan fourier transform infrared in combination with spectral simulation analysis by density functional theory

Attenuated total reflection (ATR) based dynamic compression modulation two-dimensional (2D) correlation studies of uniaxially drawn poly(ethylene terephthalate) (PET) films have been performed in combination with spectral simulation analysis by density functional theory (DFT). The dynamic 2D infrared (IR) correlation spectra in the region of the CCO stretching mode vibrations show four distinct correlation peaks located around 1290, 1265, 1248, and 1234 cm(-1). These bands can be clearly assigned to the combination bands or coupling modes of the CH in-plane bend of the benzene ring or the CH(2) deformation of the ethylene glycol unit, as well as CCO stretching vibrations, which are gauche conformer's characteristic bands, by DFT analysis. The sequential analysis of 2D correlation data shows that, upon applying the dynamic compression, the response of the side chain regions (ester groups) occurs first, followed by that of the backbone regions (benzene rings). The ATR based dynamic compression modulation 2D correlation spectroscopy in combination with DFT analysis can be a powerful tool for various polymer characterizations.     

Two-dimensional correlation analysis and waterfall plots for detecting positional fluctuations of spectral changes

In this study, we demonstrate the potentials and pitfalls of using various waterfall plots, such as conventional waterfall plots, two-dimensional (2D) gradient maps, moving window two-dimensional analysis (MW2D), perturbation-correlation moving window two-dimensional analysis (PCMW2D), and moving window principal component analysis two-dimensional correlation analysis (MWPCA2D), in the detection of the existence of band position shifts. Waterfall plots of the simulated spectral datasets are compared with conventional 2D correlation spectra. Different waterfall plots give different features in differentiating the behaviors of frequency shift versus two overlapped bands. Two-dimensional correlation spectra clearly show the very characteristic cluster pattern for both band position shifts and two overlapped bands. The vivid pattern differences are readily detectable in various waterfalls plots. Various types of waterfall plots of temperature-dependent infrared (IR) spectra of ethylene glycol, which does not have the actual band shift but only two overlapped bands, and of Fourier transform infrared (FT-IR) spectra of 2 wt% acetone in a mixed solvent of CHCl(3)/CCl(4) demonstrate that waterfall plots are not able to unambiguously detect the difference between real band shift and two overlapped bands. Thus, the presence or lack of the asynchronous 2D butterfly pattern seems like the most effective diagnostic tool for band shift detection.     

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.     

Analysis of 1H/2H exchange kinetics using model infrared spectra

This paper investigates the different approaches that best retrieve band shape parameters and kinetic time constants from series of protein Fourier transform infrared (FT-IR) spectra recorded in the course of 1H/2H exchange. In this first approach, synthetic spectra were used. It is shown that 1H/2H exchange kinetic measurements can help resolve spectral features otherwise hidden because of the overlap of various spectral contributions. We evaluated the efficiency of Fourier self-deconvolution, synchronous/asynchronous correlation, difference spectroscopy, principal component analysis, inverse Laplace transform, and determination of the underlying spectra by global analysis assuming first-order kinetics with either known or unknown time constants. It is demonstrated that some strategies allow the extraction of both the time dependence and the spectral shape of the underlying contributions.     

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.