The heterogeneity of the hydroxyl groups in chabazite

Two different clusters that have the topology of chabazite but different shapes have been used as a model for the Brønsted sites in chabazite. One of the clusters consists of eight tetrahedral atoms (8T) arranged in a ring and the other represents an intersection of two 8T rings. The adsorption of water and methanol on the two stable proton positions in chabazite has been studied using the B3LYP functional. The coordination of water and methanol with respect to the zeolite fragments were found to be similar, but with methanol situated closer to the acid site than water. The anharmonic zeolite OH stretch frequencies were found to be in the range of 2170¿2500 cm¿1 and 1457¿2074 cm¿1 in the presence of water and methanol, respectively. As a measure of the acidity of the bridging hydroxyl groups in chabazite the shift of the zeolite OH stretch frequency upon adsorption has been used. We have found that the proton attached to the oxygen atom O1 to be more acidic than the proton attached to the oxygen atom O3. Also, in the closed ring clusters the zeolite hydroxyl groups are more acidic than in the open clusters. This is not due to a steric effect as the orientation of the adsorbates with respect to the zeolite site is very similar for both clusters. The anharmonicities of the zeolite O¿H bond account for about 40% in the redshift upon the adsorption of water or methano

A DFT study of methanol adsorption in 8T rings of chabazite

Hybrid B3LYP and gradient-corrected PW91 functionals were used for studying methanol adsorption on a zeolite cluster consisting of an 8T ring of chabazite. The comparison of the results obtained with PW91 with periodic calculations has shown that the adopted ring is an adequate approximation for the Brnsted sides in chabazite. Both physisorbed and chemisorbed methanol were found to be a minimum on the potential energy surface, with an energy difference up to 10 kJ/mol in favor of the hydrogen-bonded complex. It has been shown that compared to B3LYP, the PW91 functional overestimates the hydroxyl bond distance and underestimates the hydrogen bond distance. In the physisorbed mode, the methanol oxygen atom is strongly bonded to the zeolite proton, whereas the distance between the methanol proton and the framework oxygen atoms is 1.912-2.090 Å. We have calculated for hydrogen bonded methanol hydroxyl stretch frequencies in the intervals 3677-3582 and 2358-2187 cm-1 for the methanol and the zeolite OH bonds, respectivel

Global analysis of multiple gas chromatography-mass spectrometry (GC/MS) data sets: A method for resolution of co-eluting components with comparison to MCR-ALS

Global analysis has been applied to resolve components in multiple gas chromatography-mass spectrometry (GC/MS) data sets. Global analysis methodology is based upon a parametrized model of the observed data, including random (and possibly also systematic) errors. Each elution profile is described as a function of a small number of parameters. We successfully based the description of elution profiles on an exponentially modified Gaussian. The mass spectra were described non-parametrically. Model usefulness is judged by the quality of the fit and whether the estimated parameters that describe the elution profiles and mass spectra of components are physically interpretable. Advantages of the method are most evident with multiple data sets and overlapping elution profiles. Differences between data sets are described by alignment parameters and by relative amplitude parameters. The estimated mass spectrum is identical between experiments. Global analysis and multivariate curve resolution alternating least squares (MCR-ALS) are the only methods currently developed for component resolution for the case of completely co-eluting compounds in mass spectrometry data. In the present contribution global analysis is shown to have better performance than MCR-ALS in terms of the estimated mass spectra for a variety of simulated GC mass spectrometry datasets representing components that are completely co-eluting

Quantum chemical calculation of infrared spectra of acidic groups in chabazite in the presence of water

The changes in the spectra of the acidic group in chabazite are studied by quantum chem. calcns. The zeolite is modeled by two clusters consisting of eight tetrahedral atoms arranged in a ring and seven tetrahedral atoms coordinated around the zeolite OH group. The potential energy and dipole surfaces were constructed from the zeolite OH stretch, in-plane and out-of-plane bending coordinates, and the intermol. stretch coordinate that corresponds to a movement of the water mol. as a whole. Both the anharmonicities of the potential energy and dipole were taken into account by calcn. of the frequencies and intensities. The matrix elements of the vibrational Hamiltonian were calcd. within the discrete variable representation basis set. We have assigned the exptl. obsd. frequencies at .apprx.2900, .apprx.2400, and .apprx.1700 cm-1 to the strongly perturbed zeolite OH vibrations caused by the hydrogen bonding with the water mol. The ABC triplet is a Fermi resonance of the zeolite OH stretch mode with the overtone of the in-plane bending (the A band) and the overtone of the out-of-plane bending (the C band). In the B band the stretch is also coupled with the second overtone of the out-of-plane bending. The frequencies at .apprx.3700 and .apprx.3550 cm-1 we have assigned to the OH stretch frequencies of a slightly perturbed water mol

Automated Annotation of Microbial and Human Flavonoid-Derived Metabolites

Flavonoids are a class of natural compounds essentially produced by plants that are part of animal and human diets and have assumed health-promoting benefits. Upon human consumption, these flavonoids are to a modest extent absorbed in the small intestines. The major part arrives in the colon where the microflora utilises and converts the flavonoids to a wide range of products. Many of these products are absorbed in the major intestines and subsequently metabolised by the host. To understand the impact of the microflora on the metabolism and possible effects on human health, complete (and quantitative) identification of the microbial as well as human metabolic conversion products of flavonoids is required. This is a challenging task, as these bioconversion products are often present in relatively small amounts, making classical identification strategies based on (accurate) mass information or nuclear magnetic resonance, not straightforward. In the absence of reference compounds, annotation of a component may be achieved by detailed expert evaluation, e.g. by searching for similar fragmentation patterns in spectral databases of known compounds. However, such manual analysis is a tedious task, and in advanced metabolite profiling experiments, with large numbers of unknown metabolites, this is a major bottleneck. Therefore, new strategies are needed for quick and reliable identification of the diverse range of molecules in complex matrices (faeces, blood, urine). Intelligent software for annotation and identification of unknowns is crucial to fully exploit complex datasets. We developed a new software tool (MAGMA) for (sub)structure-based annotation of LC-MSn datasets which, combined with a newly established database for phenolic molecules (MetIDB), enables semiautomated identification of flavonoid derivatives

Automated procedure for candidate compound selection in GCMS metabolomics based on prediction of Kovats retention index

Motivation: Matching both the retention index (RI) and the mass spectrum of an unknown compound against a mass spectral reference library provides strong evidence for a correct identification of that compound. Data on retention indices are, however, available for only a small fraction of the compounds in such libraries. We propose a quantitative structure - retention index model that enables the ranking and filtering of putative identifications of compounds for which the predicted RI falls outside a predefined window. Results: We constructed multiple linear regression and support vector regression (SVR) models using a set of descriptors obtained with a genetic algorithm as variable selection method. The SVR model is a significant improvement over previous models built for structurally diverse compounds as it covers a large range (360 to 4100) of RI values and gives better prediction of isomer compounds. The hit list reduction varied from 41% to 60% and depended on the size of the original hit list. Large hit lists were reduced to a greater extend compared to small hit lists

A strategy for fast structural elucidation of metabolites in small volume plant extracts using automated MS-guided LC-MS-SPE-NMR

Fast and reliable metabolite identification based on automated MS-guided HPLC-MS-SPE-NMR metabolite extraction combined with an automated 1H NMR spectrum fitting was developed. Positional isomers as structure 1 and 2 were easily distinguished. In many metabolomics studies, metabolite identification by mass spectrometry (MS) often is hampered by the lack of good reference compounds, and hence, NMR information is essential for structural elucidation, especially for the very large group of secondary metabolites. The classical approach for compound identification is to perform time-consuming and laborious HPLC fractionations and purifications, before (re)dissolving the molecules in deuterated solvents for NMR measurements. Hence, a more direct and easy purification protocol would save time and efforts. Here, we propose an automated MS-guided HPLC-MS-solid phase extraction-NMR approach, which was used to fully characterize flavonoid structures present in crude tomato plant extracts. NMR spectra of plant metabolites, automatically trapped and purified from LC-MS traces, were successfully obtained, leading to the structural elucidation of the metabolites. The MS-based trapping enabled a direct link between the mass signals and NMR peaks derived from the selected LC-MS peaks, thereby decreasing the time needed for elucidation of the metabolite structures. In addition, automated 1H NMR spectrum fitting further speeded up the candidate rejection process. Our approach facilitates the more rapid unraveling of yet unknown metabolite structures and can therefore make untargeted metabolomics approaches more powerfu

A strategy for fast structural elucidation of metabolites in small volume plant extracts using automated MS-guided LC-MS-SPE-NMR

Fast and reliable metabolite identification based on automated MS-guided HPLC-MS-SPE-NMR metabolite extraction combined with an automated 1H NMR spectrum fitting was developed. Positional isomers as structure 1 and 2 were easily distinguished. In many metabolomics studies, metabolite identification by mass spectrometry (MS) often is hampered by the lack of good reference compounds, and hence, NMR information is essential for structural elucidation, especially for the very large group of secondary metabolites. The classical approach for compound identification is to perform time-consuming and laborious HPLC fractionations and purifications, before (re)dissolving the molecules in deuterated solvents for NMR measurements. Hence, a more direct and easy purification protocol would save time and efforts. Here, we propose an automated MS-guided HPLC-MS-solid phase extraction-NMR approach, which was used to fully characterize flavonoid structures present in crude tomato plant extracts. NMR spectra of plant metabolites, automatically trapped and purified from LC-MS traces, were successfully obtained, leading to the structural elucidation of the metabolites. The MS-based trapping enabled a direct link between the mass signals and NMR peaks derived from the selected LC-MS peaks, thereby decreasing the time needed for elucidation of the metabolite structures. In addition, automated 1H NMR spectrum fitting further speeded up the candidate rejection process. Our approach facilitates the more rapid unraveling of yet unknown metabolite structures and can therefore make untargeted metabolomics approaches more powerfu

Accurate mass error correction in liquid chromatography time-of-flight mass spectrometry based metabolomics

Compound identification and annotation in (untargeted) metabolomics experiments based on accurate mass require the highest possible accuracy of the mass determination. Experimental LC/TOF-MS platforms equipped with a time-to-digital converter (TDC) give the best mass estimate for those mass signals with an intensity similar to that of the lock-mass used for internal calibration. However, they systematically underestimate the mass obtained at higher signal intensity and overestimate it at low signal intensities compared to that of the lock-mass. To compensate for these effects, specific tools are required for correction and automation of accurate mass calculations from LC/MS signals. Here, we present a computational procedure for the derivation of an intensity-dependent mass correction function. The chromatographic mass signals for a set of known compounds present in a large number of samples were reconstructed over consecutive scans for each sample. It was found that the mass error is a linear function of the logarithm of the signal intensity adjusted to the associated lock-mass intensity. When applied to all mass data points, the correction function reduced the mass error for the majority of the tested compounds to ¿1 ppm over a wide range of signal intensities. The mass correction function has been implemented in a Python 2.4 script, which accepts raw data in NetCDF format as input, corrects the detected masses and returns the corrected NetCDF files for subsequent (automated) processing, such as mass signal alignment and database searchin

MetIDB: A Publicly Accessible Database of Predicted and Experimental 1H NMR Spectra of Flavonoids

Identification of natural compounds, especially secondary metabolites, has been hampered by the lack of easy to use and accessible reference databases. Nuclear magnetic resonance (NMR) spectroscopy is the most selective technique for identification of unknown metabolites. High quality 1H NMR (proton nuclear magnetic resonance) spectra combined with elemental composition obtained from mass spectrometry (MS) are essential for the identification process. Here, we present MetIDB, a reference database of experimental and predicted 1H NMR spectra of 6000 flavonoids. By incorporating the stereochemistry, intramolecular interactions, and solvent effects into the prediction model, chemical shifts and couplings were predicted with great accuracy. A user-friendly web-based interface for MetIDB has been established providing various interfaces to the data and data-mining possibilities. For each compound, additional information is available comprising compound annotation, a 1H NMR spectrum, 2D and 3D structure with correct stereochemistry, and monoisotopic mass as well as links to other web resources. The combination of chemical formula and 1H NMR chemical shifts proved to be very efficient in metabolite identification, especially for isobaric compounds. Using this database, the process of flavonoid identification can then be significantly shortened by avoiding repetitive elucidation of already described compound