High-sensitivity visualisation of contaminants in heparin samples by spectral filtering of 1H NMR spectra

A novel application of two-dimensional correlation analysis has been employed to filter (1)H NMR heparin spectra distinguishing acceptable natural variation and the presence of foreign species. Analysis of contaminated heparin samples, compared to a dataset of accepted heparin samples using two-dimensional correlation spectroscopic analysis of their 1-dimensional (1)H NMR spectra, allowed the spectral features of contaminants to be recovered with high sensitivity, without having to resort to more complicated NMR experiments. Contaminants, which exhibited features distinct from those of heparin and those with features normally hidden within the spectral mass of heparin could be distinguished readily. A heparin sample which had been pre-mixed with a known contaminant, oversulfated chondroitin sulfate (OSCS), was tested against the heparin reference library. It was possible to recover the (1)H NMR spectrum of the OSCS component through difference 2D-COS power spectrum analysis of as little as 0.25% (w/w) with ease, and of 2% (w/w) for more challenging contaminants, whose NMR signals fell under those of heparin. The approach shows great promise for the quality control of heparin and provides the basis for greatly improved regulatory control for the analysis of heparin, as well as other intrinsically heterogeneous and varied products.     

Novel and highly sensitive mixed-polymeric electrokinetic chromatography system for determination of contaminants and impurities of heparin samples

A mixed‐polymeric electrokinetic chromatography system has been developed for the simultaneous determination of a contaminant like oversulfated condroitin sulfate (OSCS) and impurities expressed as dermatan (Der) in heparin (Hep) samples. The EKC system consisted of 0.5% w/v polymeric β‐CD, 0.4% w/v tetronic^® 1107 and 400 mM tris‐phosphate buffer at pH 3.5. The optimized electrophoretic conditions included the use of an uncoated‐silica capillary of 50 cm of total length and 75 μm id, an applied voltage of ?7 kV, a temperature of 30°C and 200 nm UV‐detection. The highly sensitive method developed showed low values of LOD, 0.07% w/w (0.07 μg/mL) (OSCS) and 0.1% w/w (0.1 μg/mL) (Der), and values of LOQ 0.2% w/w (0.2 μg/mL) (OSCS) and 0.3% w/w (0.3 μg/mL) (Der) with a concentration level of Hep sample as low as 0.1 mg/mL. Additional parameters of validation such as specificity, linearity, accuracy, and robustness were evaluated according to international guidelines. Owing to its simplicity, high sensitivity, and reliability, the proposed method can be an advantageous alternative to the traditional methodologies for the analysis of Hep in raw material and specially in finished products because of the low amounts of Hep sample required.     

Identification of proton type in concentrated sweet solutions by pulsed NMR analysis

The spin-spin proton relaxation times T₂ of concentrated sucrose, maltose,D-glucose andL-proline solutions were determined using a Bruker Minispec NMR Spectrometer. Log spin echo amplitude decay curves were also determined and their non-linear nature allowed the proportions of different proton types to be calculated. These were in agreement with the theoretical proportions of ring (non-exchangeable protons), solute hydroxyl protons and water protons in the simple sugar molecules. A deuteration experiment confirmed that only non-exchangeable ring protons remained.     

Structure of aqueous electrolyte solutions estimated by near infrared spectroscopy and chemometric analysis of spectral data

The IR spectra of aqueous solutions of MClO₄, MNO₃, MCl (M = Li, Na), CsCl, and Na₂SO₄ were measured at frequencies of 5400–7500 cm^?1 over a wide concentration range from 0.12 M up to saturation concentrations at 25°C. Chemometric analysis of the experimental data matrix was performed. Regularity was detected in distribution of the IR spectra of the studied aqueous solutions on the the plot of analysis scores by the principal component method depending on the nature of the anion-water interaction. The influence of the nature of the salt anion and cation on the water structure in aqueous electrolyte solutions was demonstrated. The existence and interconversion of the spectral forms of water with changing electrolyte concentration in solution were revealed.     

Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their <Superscript>1</Superscript>H NMR spectra

Heparin, a widely used anticoagulant primarily extracted from animal sources, contains varying amounts of galactosamine impurities. Currently, the United States Pharmacopeia (USP) monograph for heparin purity specifies that the weight percent of galactosamine (%Gal) may not exceed 1%. In the present study, multivariate regression (MVR) analysis of ¹H NMR spectral data obtained from heparin samples was employed to build quantitative models for the prediction of %Gal. MVR analysis was conducted using four separate methods: multiple linear regression, ridge regression, partial least squares regression, and support vector regression (SVR). Genetic algorithms and stepwise selection methods were applied for variable selection. In each case, two separate prediction models were constructed: a global model based on dataset A which contained the full range (0–10%) of galactosamine in the samples and a local model based on the subset dataset B for which the galactosamine level (0–2%) spanned the 1% USP limit. All four regression methods performed equally well for dataset A with low prediction errors under optimal conditions, whereas SVR was clearly superior among the four methods for dataset B. The results from this study show that ¹H NMR spectroscopy, already a USP requirement for the screening of contaminants in heparin, may offer utility as a rapid method for quantitative determination of %Gal in heparin samples when used in conjunction with MVR approaches.     

Modern strategies for PC-supported spectral analysis of 1D NMR data

 Analysis of high-resolution NMR spectra elucidation has been known for many years. Hard-and software development now permits the implementation of such programs on personal computers. The structural information hidden in complex proton NMR spectra becomes easily accessible by using graphical user interfaces and direct data exchange between programs. A new mode has been implemented in 1D WIN-NMR to support the analysis of multiplet patterns with first order rules. Structure display, direct export mechanisms to the simulation program WIN-DAISY, and an archiving possibility complete the state-of-the-art data analysis. Some practical examples are given. Received: 25 October 1996/Revised: 6 March 1997/Accepted: 10 March 1997     

Identification of components in mixtures by a mathematical analysis of mass spectral data

Mathematical techniques for the identification of components in mixtures from the mass spectra of a series of related mixtures are described. The approach is analogous to library search methods in that spectra from a reference collection are compared with a multidimensional unknown. Searches are conducted with a library file containing approximately 17000 mass spectra. Results for the analyses of several mixtures are reported, to illustrate the effectiveness of the method.     

Factors that affect quantification of diode array data in comprehensive two-dimensional liquid chromatography using chemometric data analysis

There has been a tremendous increase in research on comprehensive two dimensional LC (LC×LC); however, to date, the central analytical issue, quantification, has received only minimal attention. It is vital to the further development of LC×LC that a greater understanding of the specific factors affecting peak quantification in LC×LC be attained. This work focuses on the following factors: data complexity, retention time shifting, dynamic range issues, chromatographic and spectral peak overlap and difficulties related to background signal removal. The above mentioned factors that affect peak quantification are investigated using fourteen replicate analyses of a urine sample, representing the effects of such factors when analyzing samples in complex matrices. We demonstrate that quantification of LC×LC data is improved following implementation of chemometric techniques that minimized the deleterious effects on quantification due to chromatographically overlapped peaks, retention time shifting and background signal interference. The chemometrically resolved data shows a 2.5-fold increase in precision of quantification over the quantification of the raw data. It is also demonstrated that the method quantifies sixteen peaks that were not visually evident prior to chemometric analysis. The purpose of this paper is to determine the impact of these issues on the effectiveness of LC×LC as a technique for the quantitative analysis of complex samples.     

Identification of polymers by library search of pyrolysis mass spectra and pattern recognition analysis

A library of mass spectra of polymers containing about 200 entries is described. These spectra were obtained by direct pyrolysis-electron-impact mass spectrometry, i.e., by heating the polymers in the direct insertion probe for solid samples at a constant heating rate and recording repetitive mass spectra during the temperature rise. The library can be used both as a data base for library searches and as a training set for a pattern recognition analysis. The algorithm used to generate and search the files and a few applications of the library search and pattern recognition analysis are presented.     

Identification of biochemical changes in lactovegetarian urine using 1H NMR spectroscopy and pattern recognition

A vegetarian diet has been demonstrated to have a profound influence on human metabolism as well as to aid the prevention of several chronic diseases relative to an omnivorous diet. However, there have been no systematic metabolomic studies on all of the biochemical changes induced in human subjects by long-term vegetarianism. In this study, ¹H NMR spectroscopy in combination with multivariate statistical analysis was applied to explore the variability in the metabolic urinary profiles of healthy populations from four groups: lactovegetarian male (VEGMALE), lactovegetarian female (VEGFEMALE), omnivorous male (OMNMALE), and omnivorous female (OMNFEMALE). Differences in metabolic profiles were examined in relation to diet and gender by principal component analysis (PCA) and spectral integrals. It was found that the most influential low molecular weight metabolites responsible for the differences between the diet groups were N-acetyl glycoprotein (NAG), succinate, citrate, trimethylamine-N-oxide (TMAO), taurine, glycine, hippurate, phenylalanine, methylhistidine and formate, whereas for the differences in gender groups the most discriminatory metabolites were NAG, succinate, creatinine, arginine, TMAO, taurine, hippurate, mannitol, phenylalanine, and methylhistidine. The results from the PCA of all four groups indicated that diet plays a greater role in influencing metabolite differences than gender. As an exploration, this work shows the potential of metabolomics when applied to nutritional and physiological studies, and it will aid further studies.     

Extraction of information from large data sets by pattern recognition

The use of computers has made data collection much easier and analytical chemists increasingly wonder how to make use of all the data obtained. Pattern recognition permits to extract information present in large data sets in an automatic way.Many scientists acknowledge this fact but are rebutted by the task of learning to use pattern recognition methods. Indeed, there are many methods available and for the newcomer it is extremely difficult to make a selection. For this reason, the lecture will start by explaining the models used in pattern recognition. This will be followed by a critical discussion of advantages and disadvantages of the methods and a selection of preferred methods.

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Gewinnung von Information aus großen Datenmengen mit Hilfe der Strukturerkennung

     

Extraction of information from large data sets by pattern recognition

Summary Pattern recognition permits to extract information present in large data sets in an automatic way. Many scientists acknowledge this fact but are rebutted by the task of learning to use pattern recognition methods. Indeed, there are many methods available and for the newcomer it is extremely difficult to make a selection. For this reason, the paper starts by explaining the models used in pattern recognition. This is followed by a critical discussion of advantages and disadvantages of the methods and a selection of preferred methods.

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Gewinnung von Information aus großen Datenmengen mit Hilfe der Mustererkennung

Zusammenfassung Mit Hilfe der Mustererkennung können Informationen aus großen Datenmengen automatisch gewonnen werden. Obwohl man sich dessen allgemein bewußt ist, schreckt man doch gewöhnlich vor der Aufgabe zurück, sich mit den entsprechenden Methoden befassen zu müssen, denn es gibt sehr viele davon und eine geeignete Auswahl ist schwer zu treffen. Aus diesem Grund werden in dieser Arbeit die einzelnen Verfahren der Mustererkennung erklärt, deren Vor- und Nachteile diskutiert und eine entsprechende Auswahl geboten.

     

Processing of NMR,UV, and IR spectrometric data prior to chemometric simulation by independent component and principal component analysis

We consider methods for the mathematical preprocessing of signals in the spectrometric analysis of multicomponent mixtures using chemometric algorithms aimed at adjusting the baseline, experimental noise, and random shift of spectral bands. Practical examples of using simple mathematical operations (scaling, centering, derivatization) are given. The effectiveness of algorithms is illustrated by a wide range of spectroscopic signals (electronic absorption, IR, and NMR spectra) combined with chemometric methods of principal component analysis and independent component analysis.     

Application to classification of mulberry leaves using multivariate analysis of proton NMR metabolomic data

Recently, NMR-based metabolomic analysis has been used to acquire information based on differentiation among biological samples. In the present study, we examined whether multivariate analysis was able to be applied to natural products and/or material field. Each extraction of 24 leaf samples, divided into six locations from the tip of the stem in each of four strains, was analyzed by pattern recognition methods, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). Twenty-four extracts from mulberry leaf showed independent spectra by 1H NMR. The separation of leaf extraction data due to the difference at six locations was achieved in the PCA score plot as correlation PC1 (86.1%) and PC3 (4.6%) and showed two loading plots, suggesting classification by leaf position as an independent variable in the loading plot. Moreover, the difference among six locations clarified the seven highest discrimination powers by the SIMCA method. Meanwhile, the PCA score plot obtained classification by the variety of mulberry strains with three loading plots, but the SIMCA method did not give a peak by classification.     

Identification of oil spills in harbours by means of pattern recognition

For environmental control purposes, floating oil spills in Europoort (Rotterdam) harbours must often be identified and their sources located. Pattern recognition, applied to gas chromatographic data for the spill and for various suspected sources such as oil from bunkers of passing ships and from harbour installations, can lead to definite conclusions, particularly after artificial weathering formulae have been used. The general approach for tackling these problems and the complicating factor of weathering of the oil spills is discussed.     

Determination of Ta impurities in Nb samples by X-rays in neutron activation analysis

Ta impurities in Nb samples have been determined by X-rays in 14 MeV neutron activation analysis. For X-ray detection, (HP) Ge detector with Be-window is used. The sensitivity of the determination has been found to be about 30 g (in Nb).This work was supported by the National Foundation for Research (Contract No. 259).     

Thermodynamic characterization of the dimerization equilibrium of an asymmetric dye by spectral titration and chemometric analysis

The monomer-dimer equilibrium of an asymmetric cyanine dye has been investigated by means of UV-Vis spectroscopy. The data have been processed by a recently developed chemometric method for quantitative analysis of undefined mixtures, that is based on simultaneous resolution of the overlapping bands in the whole set of absorption. In this work the dimerization constant of 1-carboxydecyl-4-{3-[3-methyl-3H-benzothiazol-2-ylidene]-propenyl}-quinolinium (TO-3) has been determined by studying the dependence of absorption spectrum on temperature in the range 25-72.5 °C at different total concentrations of dye (8.5×10⁻⁶ to 2.87×10⁻⁵ M). Utilizing the van’t Hoff relation, which describes the dependence of the equilibrium constant on temperature, as constraint we determine the spectral responses of the monomer and dimer species as well as the enthalpy and entropy of the dimerization equilibrium.     

Assignment of proton NMR spectra and conformational analysis by two-dimensional heteronuclear relayed correlation NMR spectroscopy

The method of two-dimensional heteronuclear relayed correlation spectroscopy was used to establish the assignment of the severely overcrowded part of the proton spectrum of menthol by relating it to the previously assigned carbon spectrum. Extrapolation of the signal-to-noise ratio obtained with overnight data accumulation on a 10 mM solution suggests that this experiment should be feasible on as little as 10 mg of a moderate-sized organic compound.