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.     

Correlation coefficient mapping in fluorescence spectroscopy: tissue classification for cancer detection

Correlation coefficient mapping has been applied to intrinsic fluorescence spectra of colonic tissue for the purpose of cancer diagnosis. Fluorescence emission spectra were collected of 57 colonic tissue sites in a range of 4 physiological conditions: normal (29), hyperplastic (2), adenomatous (5), and cancerous tissues (21). The sample-sample correlation was used to examine the ability of correlation coefficient mapping to determine tissue disease state. The correlation coefficient map indicates two main categories of samples. These categories were found to relate to disease states of the tissue. Sensitivity, selectivity, predictive value positive, and predictive value negative for differentiation between normal tissue and all other categories were all above 92%. This was found to be similar to, or higher than, tissue classification using existing methods of data reduction. Wavelength-wavelength correlation among the samples highlights areas of importance for tissue classification. The two-dimensional correlation map reveals absorption by NADH and hemoglobin in the samples as negative correlation, an effect not obvious from the one-dimensional fluorescence spectra alone. The integrity of tissue was examined in a time series of spectra of a single tissue sample taken after tissue resection. The wavelength-wavelength correlation coefficient map shows the areas of significance for each fluorophore and their relation to each other. NADH displays negative correlation to collagen and FAD, from the absorption of emission or fluorescence resonance energy transfer. The wavelength-wavelength correlation map for the decay set also clearly shows that there are only three fluorophores of importance in the samples, by the well-defined pattern of the map. The sample-sample correlation coefficient map reveals the changes over time and their impact on tissue classification. Correlation coefficient mapping proves to be an effective method for sample classification and cancer detection.     

Sample-sample and wavenumber-wavenumber two-dimensional correlation analyses of attenuated total reflection infrared spectra of polycondensation reaction of bis(hydroxyethyl terephthalate).

Polycondensation reaction of bis(hydroxyethylterephthalate) was monitored in situ by attenuated total reflection (ATR)/infrared (IR) spectroscopy. The obtained spectra are analyzed by means of generalized two-dimensional (2D) sample-sample and wavenumber-wavenumber correlation spectroscopies. The sample-sample correlation analysis reveals the correlations among the concentration features of the components, and the wavenumber-wavenumber correlation analysis elucidates the relations among the spectral features. Before the experimental data are analyzed by the 2D correlation spectroscopies, a synthetic two-component spectral model composed of the first and the last experimental spectra, is examined. The results of an analysis of the real data are related to those obtained from the synthetic data. It is found that the sample-sample correlation analysis of the IR data of polycondensation explains the concentration variance in the system and classifies two groups of the samples. The wavenumber-wavenumber correlations are derived upon the results of the sample-sample correlations and explained in terms of the spectral variations of three components. The convolute patterns in both types of correlations are attributed to the weak presence of ethylene glycol.     

Application of sample-sample two-dimensional correlation spectroscopy to determine the glass transition temperature of poly(ethylene terephthalate) thin films

The glass transition temperatures (Tg) of poly(ethylene terephthalate) (PET) thin films with different thicknesses are determined by analyzing their in situ reflection-absorption infrared (RAIR) spectra measured over a temperature range of 28 to 84 degrees C. The criterion of standard deviation of the covariance matrices is used as a graphical indicator for the determination of the Tg present in the sample-sample two-dimensional (2D) correlation spectra calculated from the temperature-dependent RAIR spectra. After two data pretreatments of the first derivative of the spectral absorbance versus temperature and the mean normalization over the wavenumbers are sequentially carried out on the RAIR spectra, an abrupt change of the first-derivative correlation spectra with respect to temperature is quickly obtained. It reflects the temperature at which the apparent intensity changes in pertinent absorption bands of PET thin films take place due to the dramatic segmental motion of PET chain conformation. The Tg of the thin PET films is accordingly determined. The results reveal that it decreases with a great dependence on the film thickness and that sample-sample 2D correlation spectroscopy enables one to determine the transition temperature of polymer thin films in an easy and valid way.     

Multivariate curve resolution applied to the analysis and resolution of two-dimensional [1H,15N] NMR reaction spectra

Multivariate curve resolution is proposed for the study of complex chemical reactions monitored by two-dimensional (2D) NMR spectroscopy. In particular, in this work, multivariate curve resolution is applied to the study of the reaction between (15)N-labeled cisplatin and the amino acid-nucleotide hybrid (Phac-Met-linker-p(5')dG). At several stages of the reaction, 2D [(1)H,(15)N] HSQC NMR spectra were acquired and stored in data matrices. In a first step, multivariate curve resolution was applied to analyze individually each one of these 2D spectra, allowing the resolution of the corresponding (1)H and (15)N one-dimensional correlation spectra. In a second step, the whole set of 2D spectra recorded along the reaction were simultaneously analyzed by multivariate curve resolution, allowing the resolution of the kinetic concentration profiles and of the pure 2D NMR spectra of each of the species detected along the reaction. Results finally obtained confirmed previously postulated reaction mechanisms involving the existence of two monofunctional adducts and of two bifunctional adducts, with the structure of one of them not completely resolved.     

2D correlation spectroscopy and multivariate curve resolution in analyzing pH-dependent evolving systems monitored by FT-IR spectroscopy,a comparative study

Multivariate curve resolution (MCR) and 2D correlation spectroscopy (2D-CoS), including sample-sample correlation, have been applied to the analysis of evolving midinfrared spectroscopic data sets obtained from titrations of organic acids in aqueous solution. In these data sets, well-defined species with significant differences in their spectra are responsible for the spectral variation observed. The two fundamentally different chemometric techniques have been evaluated and discussed on the basis of experimental and supportive simulated data sets. MCR gives information that can be directly related to the chemical species that is of importance from a practical point of view, whereas 2D-CoS results normally require more interpretation. The obtained conclusions are regarded valid for similar evolving data, which are increasingly being encountered in analytical chemistry when multivariate detectors are used to follow dynamic processes, including separations as well as chemical reactions, among others.     

Analyzing Raman maps of pharmaceutical products by sample-sample two-dimensional correlation

Sample-sample (SS) two-dimensional (2D) correlation spectroscopy is applied in this study as a spectral selection tool to produce chemical images of real-world pharmaceutical samples consisting of two, three, and four components. The most unique spectra in a Raman mapping spectral matrix are found after analysis of the covariance matrix. (This is obtained by multiplying the original mapping data matrix by itself.) These spectra are identified by analyzing the slices of the covariance matrix at the positions where covariance values are at maxima. Chemical images are subsequently produced in a univariate fashion by visually selecting the wavenumbers in the extracted spectra that are least overlapped. The performance of SS 2D correlation is compared with principal component analysis in terms of highlighting the most prominent spectral differences across the whole data set (which typically comprises several thousand spectra) and determining the total number of species present. In addition, the selection of the unique spectra by SS 2D correlation is compared with the selection obtained by the orthogonal projection approach (OPA). Both comparisons are found to be satisfactory and demonstrate that a quite simple SS 2D correlation routine can be used for producing reliable images of unknown samples. The main benefit of using SS 2D correlation is that it is based on a few data processing commands that can be executed separately and produce results that are closely related to the chemical features of the system.     

Moving window two-dimensional correlation spectroscopy and determination of signal-to-noise threshold in correlation spectra

In this paper we report two new developments in two-dimensional (2D) correlation spectroscopy; one is the combination of the moving window concept with 2D spectroscopy to facilitate the analysis of complex data sets, and the other is the definition of the noise level in synchronous/asynchronous maps. A graphical criterion for the latter is also proposed. The combination of the moving window concept with correlation spectra allows one to split a large data matrix into smaller and simpler subsets and to analyze them instead of computing overall correlation. A three-component system that mimics a consecutive chemical reaction is used as a model for the illustration of the two ideas. Both types of correlation matrices, variable-variable and sample-sample, are analyzed, and a very good agreement between the two is met. The proposed innovations enable one to comprehend the complexity of the data to be analyzed by 2D spectroscopy and thus to avoid the risks of over-interpretation, liable to occur whenever improper caution about the number of co-existing species in the system is taken.     

Hierarchical Alignment and Full Resolution Pattern Recognition of 2D NMR Spectra: Application to Nematode Chemical Ecology

Nuclear magnetic resonance (NMR) is the most widely used nondestructive technique in analytical chemistry. In recent years, it has been applied to metabolic profiling due to its high reproducibility, capacity for relative and absolute quantification, atomic resolution, and ability to detect a broad range of compounds in an untargeted manner. While one-dimensional (1D) (1)H NMR experiments are popular in metabolic profiling due to their simplicity and fast acquisition times, two-dimensional (2D) NMR spectra offer increased spectral resolution as well as atomic correlations, which aid in the assignment of known small molecules and the structural elucidation of novel compounds. Given the small number of statistical analysis methods for 2D NMR spectra, we developed a new approach for the analysis, information recovery, and display of 2D NMR spectral data. We present a native 2D peak alignment algorithm we term HATS, for hierarchical alignment of two-dimensional spectra, enabling pattern recognition (PR) using full-resolution spectra. Principle component analysis (PCA) and partial least squares (PLS) regression of full resolution total correlation spectroscopy (TOCSY) spectra greatly aid the assignment and interpretation of statistical pattern recognition results by producing back-scaled loading plots that look like traditional TOCSY spectra but incorporate qualitative and quantitative biological information of the resonances. The HATS-PR methodology is demonstrated here using multiple 2D TOCSY spectra of the exudates from two nematode species: Pristionchus pacificus and Panagrellus redivivus. We show the utility of this integrated approach with the rapid, semiautomated assignment of small molecules differentiating the two species and the identification of spectral regions suggesting the presence of species-specific compounds. These results demonstrate that the combination of 2D NMR spectra with full-resolution statistical analysis provides a platform for chemical and biological studies in cellular biochemistry, metabolomics, and chemical ecology.     

Two-dimensional infrared spectroscopy and principal component analysis studies on a new azobenzene derivative supramolecular system based on hydrogen bonds

Infrared (IR) spectra of a supramolecular assembly with an azobenzene derivative and intermolecular hydrogen bonds have been measured in the temperature range from 30 to 200 degrees C to investigate heat-induced structural changes and thermal stability. Principal component analysis (PCA) and two kinds of two-dimensional (2D) correlation spectroscopy, variable-variable (VV) 2D and sample-sample (SS) 2D spectroscopy, have been employed to analyze the observed temperature-dependent spectral variations. The PCA and SS 2D correlation analyses have demonstrated that the complete decoupling of hydrogen bonds in the supramolecular assembly occurs between 110 and 115 degrees C, which is in good agreement with the results of a differential scanning calorimetry (DSC) study for the heating process. The PCA of the IR spectra in the region of 3600-3100 cm(-1) has illustrated that there are at least four principal components for the different NH2 and CONH species in the present supramolecular system. The VV 2D correlation spectroscopy study has provided information about the structure and strength of hydrogen bonds of NH2 and CONH groups and their temperature-dependent variations. The different species of hydrogen-bonded NH2 and CONH groups in the supramolecular system can be clarified by the VV 2D correlation analysis. The VV 2D correlation analysis has also revealed the specific order of the temperature-induced changes in the hydrogen bonds of NH2 and CONH groups.     

Heteronuclear 19F-1H statistical total correlation spectroscopy as a tool in drug metabolism: study of flucloxacillin biotransformation

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.     

Statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples

Although NMR spectroscopic techniques coupled with multivariate statistics can yield much useful information for classifying biological samples based on metabolic profiles, biomarker identification remains a time-consuming and complex procedure involving separation methods, two-dimensional NMR, and other spectroscopic tools. We present a new approach to aid complex biomixture analysis that combines diffusion ordered (DO) NMR spectroscopy with statistical total correlation spectroscopy (STOCSY) and demonstrate its application in the characterization of urinary biomarkers and enhanced information recovery from plasma NMR spectra. This method relies on calculation and display of the covariance of signal intensities from the various nuclei on the same molecule across a series of spectra collected under different pulsed field gradient conditions that differentially attenuate the signal intensities according to translational molecular diffusion rates. We term this statistical diffusion-ordered spectroscopy (S-DOSY). We also have developed a new visualization tool in which the apparent diffusion coefficients from DO spectra are projected onto a 1D NMR spectrum (diffusion-ordered projection spectroscopy, DOPY). Both methods either alone or in combination have the potential for general applications to any complex mixture analysis where the sample contains compounds with a range of diffusion coefficients.     

Noise reduction methods applied to two-dimensional correlation spectroscopy (2D-COS) reveal complementary benefits of pre- and post-treatment

Two-dimensional correlation spectroscopy (2D-COS) is a powerful spectral analysis technique widely used in many fields of spectroscopy because it can reveal spectral information in complex systems that is not readily evident in the original spectral data alone. However, noise may severely distort the information and thus limit the technique's usefulness. Consequently, noise reduction is often performed before implementing 2D-COS. In general, this is implemented using one-dimensional (1D) methods applied to the individual input spectra, but, because 2D-COS is based on sets of successive spectra and produces 2D outputs, there is also scope for the utilization of 2D noise-reduction methods. Furthermore, 2D noise reduction can be applied either to the original set of spectra before performing 2D-COS ("pretreatment") or on the 2D-COS output ("post-treatment"). Very little work has been done on post-treatment; hence, the relative advantages of these two approaches are unclear. In this work we compare the noise-reduction performance on 2D-COS of pretreatment and post-treatment using 1D (wavelets) and 2D algorithms (wavelets, matrix maximum entropy). The 2D methods generally outperformed the 1D method in pretreatment noise reduction. 2D post-treatment in some cases was superior to pretreatment and, unexpectedly, also provided correlation coefficient maps that were similar to 2D correlation spectroscopy maps but with apparent better contrast.     

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.     

Deconvolution of chemical mixtures with high complexity by NMR consensus trace clustering

Identification and quantification of analytes in complex solution-state mixtures are critical procedures in many areas of chemistry, biology, and molecular medicine. Nuclear magnetic resonance (NMR) is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of the samples. We present three new multidimensional-NMR based approaches that are geared toward the analysis of mixtures with high complexity at natural (13)C abundance, including approaches that are encountered in metabolomics. Common to all three approaches is the concept of the extraction of one-dimensional (1D) consensus spectral traces or 2D consensus planes followed by clustering, which significantly improves the capability to identify mixture components that are affected by strong spectral overlap. The methods are demonstrated for covariance (1)H-(1)H TOCSY and (13)C-(1)H HSQC-TOCSY spectra and triple-rank correlation spectra constructed from pairs of (13)C-(1)H HSQC and (13)C-(1)H HSQC-TOCSY spectra. All methods are first demonstrated for an eight-compound metabolite model mixture before being applied to an extract from E. coli cell lysate.     

Web server based complex mixture analysis by NMR

Comprehensive metabolite identification and quantification of complex biological mixtures are central aspects of metabolomics. NMR shows excellent promise for these tasks. An automated fingerprinting strategy is presented, termed COLMAR query, which screens NMR chemical shift lists or raw 1D NMR cross sections taken from covariance total correlation spectroscopy (TOCSY) spectra or other multidimensional NMR spectra against an NMR spectral database. Cross peaks are selected using local clustering to avoid ambiguities between chemical shifts and scalar J-coupling effects. With the use of three different algorithms, the corresponding chemical shift list is then screened against chemical shift lists extracted from 1D spectra of a NMR database. The resulting query scores produced by forward assignment, reverse assignment, and bipartite weighted-matching algorithms are combined into a consensus score, which provides a robust means for identifying the correct compound. The approach is demonstrated for a metabolite model mixture that is screened against the metabolomics BioMagResDatabank (BMRB). This NMR-based compound identification approach has been implemented in a public Web server that allows the efficient analysis of a wide range of metabolite mixtures.     

Heteronuclear 1H-31P statistical total correlation NMR spectroscopy of intact liver for metabolic biomarker assignment: application to galactosamine-induced hepatotoxicity

As part of our ongoing development of methods for enhanced biomarker information recovery from spectroscopic data we present the first example of a new hetero-nuclear statistical total correlation spectroscopy (HET-STOCSY) approach applied to intact tissue samples collected as part of a toxicological study. One-dimensional 1H and 31P-{1H} magic angle spinning (MAS) NMR spectra of intact liver samples after galactosamine (galN) treatment to rats and after cotreatment of galN plus uridine were collected at 275 K. Individual samples were also followed by 1H and 31P-{1H} MAS NMR through time generating time dependent modulations in metabolite signatures relating to toxicity. High-resolution 1H NMR spectra of urine and plasma and clinical chemical data were also collected to establish a biological framework in which to place these novel statistical heterospectroscopic data. In HET-STOCSY, calculation of the covariance between the 31P-{1H} and 1H NMR signals of phosphorus containing metabolites allows their molecular connectivities to be established and the construction of virtual two-dimensional heteronuclear correlation spectra that connect all protons on the molecule to the heteroatom. We show how HET-STOCSY applied to MAS NMR spectra of liver samples can be used to augment biomarker detection. This approach is generic and can be applied to correlate the covarying signals for any spin-active nuclei where there is parallel or serial collection of data.     

Two-dimensional correlation analysis of spectroelectrochemical data for p-benzoquinone reduction in acetonitrile

Two-dimensional (2D) spectral correlation analysis has been employed to interpret the complex spectroelectrochemical data obtained from an electrochemical system undergoing following reactions after electron transfer. The system used was electrochemical reduction of p-benzoquinone (p-BQ) in acetonitrile, which produces anion radicals and dianions at its first and second reduction potentials. The dianions undergo a fast comproporationation reaction with neutral p-BQ molecules to produce anion radicals back, complicating the spectral analysis. Upon application of 2D correlation analysis in conjunction with the self-modeling curve resolution technique, we were able not only to resolve the spectra and determine the sequence of spectral emergence but also to extract the individual spectra. The techniques offer a very powerful tool for interpreting highly convoluted spectra obtained from a system where a series of chemical reactions occur following the electron transfer at the electrode/electrolyte interface.     

Mechanism of thermal phase transition of a ferroelectric liquid crystal with monotropic transition temperature studied by infrared spectroscopy combined with principal component analysis and sample-sample two-dimensional correlation spectroscopy

Infrared (IR) spectra of FLC-154 (FLC: ferroelectric liquid crystal) with monotropic phase transition under a nonalignment state with a sample layer thickness of 24.5 microm were measured for heating process from 55 to 90 degrees C and a cooling process from 90 to 55 degrees C in increments of 1 degrees C. The thermal dynamics of FLC-154 were investigated by use of IR spectroscopy combined with principal component analysis (PCA) and sample-sample two-dimensional (2D) correlation spectroscopy. During the cooling, the FLC-154 molecule passes through the monotropic smectic-C* (Sm-C*) phase, which is transformed from the Sm-A phase. The results from PCA suggest that during the heating process, the thermal dynamics of the alkyl chains, core moiety, and C=O groups are similar to each other. Furthermore, PCA and sample-sample 2D correlation spectroscopy indicate that the alkyl chains and C=O groups in the chiral and core moieties are responsible for the emergence of the Sm-C* phase. This conclusion is very important because the IR data have given more evident cause for the emergence of the Sm-C* phase than the theoretical models such as the molecular-statistical theory of ferroelectric ordering and the indigenous polarization theory. Moreover, it has been found that some of the trans conformations of the alkyl chains of FLC-154 change partly to the gauche conformation when the phase transition from the crystalline phase to the Sm-A phase occurs. It has also been found that the intermolecular interactions of the C=O group in the core moiety in the Sm-A phase are weaker than those in the crystalline phase and that the conformational change occurs on the C-O-C bonds in the core moiety upon going from the crystalline to the Sm-A phase.     

Kinetic and J-resolved statistical total correlation NMR spectroscopy approaches to structural information recovery in complex reacting mixtures: application to acyl glucuronide intramolecular transacylation reactions

We demonstrate here a new variant on a statistical spectroscopic method for recovering structural information on unstable intermediates formed in reaction mixtures. We exemplify this approach with respect to the internal acyl migration reactions of 1-beta-O-acyl glucuronides (AGs), which rearrange at neutral or slightly alkaline pH on a minute to hour time scale to yield a series of positional glucuronide ring isomers and alpha/beta anomers from the 1-beta (starting material), i.e. 2-beta, 2-alpha, 1-alpha, 3-beta, 3-alpha, and 4-beta, 4-alpha isomers together with the aglycon and alpha- and beta-glucuronic acid hydrolysis products. Multiple sequential 800 MHz cryoprobe (1)H NMR spectra (1D and 2D J-resolved, JRES) were collected on a 5.1 mM solution of a synthetic model drug glucuronide, 1-beta-O-acyl (S)-alpha-methyl phenylacetyl glucuronide (MPG) in 0.1 M sodium phosphate buffer in D2O at pD 7.4 over 18 h to monitor the reaction which leads to the formation of the eight positional isomers and hydrolysis products. As the reaction proceeds and new isomers form, the NMR signal intensities vary accordingly allowing the application of a novel kinetic variant on statistical total correlation spectroscopy (K-STOCSY) method to recover the connectivities between proton signals on the same reacting molecule based on their intensity covariance through time. We performed K-STOCSY analysis on both the standard 1D NMR spectra and the skyline projected singlets of the (1)H-(1)H JRES NMR spectra through time, i.e. the K-JRES-STOCSY experimental variant, which increases the effective spectral dispersion and is ideally suited for the analysis of heavily overlapped spin systems. High statistical correlations were observed between mutarotated alpha- and beta-anomers of individual positional isomers, as well as directly acyl migrated products and anticorrelation observed between signals from compounds that were being depleted as others increased, e.g. between the 1-beta and 2-alpha/2-beta isomers. This statistical kinetic approach enabled the recovery of structural connectivity information on all isomers allowing unequivocal resonance assignment, and this approach to spectroscopic information recovery has wider potential uses in the study of reactions that occur on the second-to-minute time scale in conditions where multiple sequential NMR spectra can be collected. JRES-STOCSY is also of potential use as a method for recovering spectroscopic information in highly overlapped NMR signals and spin systems in other types of complex mixture analysis.