Explorative data analysis of two-dimensional electrophoresis gels

Methods for classification of two-dimensional (2-DE) electrophoresis gels based on multivariate data analysis are demonstrated. Two-dimensional gels of ten wheat varieties are analyzed and it is demonstrated how to classify the wheat varieties in two qualities and a method for initial screening of gels is presented. First, an approach is demonstrated in which no prior knowledge of the separated proteins is used. Alignment of the gels followed by a simple transformation of data makes it possible to analyze the gels in an automated explorative manner by principal component analysis, to determine if the gels should be further analyzed. A more detailed approach is done by analyzing spot volume lists by principal components analysis and partial least square regression. The use of spot volume data offers a mean to investigate the spot pattern and link the classified protein patterns to distinct spots on the gels for further investigation. The explorative approach in analysis of 2-D gels makes it possible, in a fast and convenient way, to screen many gels in order to determine the protein patterns that form clusters and could be selected for further examination.     

Monofluorinated analogues of polycyclic aromatic hydrocarbons as internal standards for GC-MS in environmental analysis

Summary In the gas chromatographic determination of polycyclic aromatic hydrocarbons (PAHs), internal standards (ISs) are frequently used in order to correct for losses and fluctuating experimental parameters. In this study eight monofluorinated PAHs (F-PAHs) are used to this end. Analysis was by means of large-volume injection-gas chromatography with mass spectrometric detection (LVI-GC-MS). Relevant experimental results and performance data are presented, and it is demonstrated that the use of at least one F-PAH as IS improves the repeatability dramatically. As a brief application, F-PAHs were used as ISs for the trace-level determination of PAHs in soil, after extraction by means of pressurized liquid extraction (PLE) and liquid partitioning (LP). The suitability of the approach was demonstrated with naphthalene and pyrene as test compounds.     

A multivariate chemometric approach to fluorescence spectroscopy

A multivariate approach to the solution of problems often encountered in the spectrofluorometry of natural samples, utilising information from whole spectra is presented. (a) Piecewise direct standardisation is implemented and employed to transfer emission spectra measured with two different xenon lamps of different ages as if the spectra were measured with the same lamp. (b) It has been shown using a multivariate analysis approach that it is possible to use the raw data points instead of the smoothed data based on an algorithm included in the instrument software by the manufacturer. (c) It is documented that Raman scattering does not hamper the performance of multivariate calibration; on the contrary, in an experiment with sugar samples the concentration prediction errors become about five times lower by including the whole emission spectrum in the analysis instead of using a univariate calibration based on an emission wavelength that only reflects the analyte of interest. (d) An algorithm for variable selection is implemented and employed in the selection of optimal excitation wavelengths. Among 13 emission spectra recorded for a sugar sample at different excitation wavelengths, four of these are chosen that describe 98.51% of the total variance in the original data. (e) Finally the combination of fluorescence spectroscopy and multivariate calibration with conventional chemical data according to the near-infrared black box model is presented. The refined sugar quality parameter, the ash content and the fluorescence emission spectra are correlated by a partial least-squares regression model. Five experiments employing different monochromator slit widths and sugar concentrations are performed, and the best correlation obtained by full cross-validation of the 15 sugar samples is R = 0.98.     

A new parameter-free correlation functional based on an average atomic reduced density gradient analysis

A new parameter-free correlation functional based on the local Ragot-Cortona approach [J. Chem. Phys. 121, 7671 (2004)] is presented. This functional rests on a single ansatz for the gradient correction enhancement factor: it is assumed to be given by a simple analytic expression satisfying some exact conditions and containing two coefficients. These coefficients are determined without implementing the functional and without using a fitting procedure to experimental data. Their values are determined by requiring that the functional gives a correct average reduced density gradient for atoms, which, to some extent, can be considered an intrinsic atomic property. The correlation functional is then coupled with the Perdew-Burke-Erzernhof (PBE) exchange and compared with the original PBE approach as well as with some other pure density or hybrid approaches. Standard tests for atomic and molecular systems show that our new functional significantly improves on PBE, showing very interesting properties.     

Quantitative 2D HSQC (Q-HSQC) via suppression of J-dependence of polarization transfer in NMR spectroscopy: application to wood lignin

A quantitative method to record (1)H-(13)C correlation NMR spectra (Q-HSQC) is presented. The suppression of (1)J(CH)-dependence is achieved by modulating the polarization transfer delays of HSQC. In addition, the effect of homonuclear couplings, as well as relaxation during the pulse sequence are discussed. We developed the Q-HSQC approach for the quantitative analysis of wood lignin, a complex polymer where it has been difficult to obtain reliable data on the relative amounts of different structural units. The current method is applicable to a variety of complex mixtures, where normal 1D (1)H- and (13)C-NMR methods fail.     

A multivariate representation and analysis of DNA sequence data

A new way to represent and analyze DNA sequence data is described. This approach complements methods currently used, in that it allows the systematic part of the variation between different sequences to be modeled. This can prove as informative as absence of variation (homology), which is the most widely used criterion for comparing sequence data. A multivariate sequence-activity model (SAM), for DNA-promoter sequences is presented, by which the relative promoter strength is modeled in terms of the primary DNA-sequence. The model is shown to have a good predictive capability. The coefficients from the model are interpreted, and used to design new structures predicted to be strong promoters in the system investigated. The approach described is also applicable to other kinds of sequence data, e.g. RNAs, proteins or peptides.     

A multiscale coarse-graining method for biomolecular systems

A new approach is presented for obtaining coarse-grained (CG) force fields from fully atomistic molecular dynamics (MD) trajectories. The method is demonstrated by applying it to derive a CG model for the dimyristoylphosphatidylcholine (DMPC) lipid bilayer. The coarse-graining of the interparticle force field is accomplished by an application of a force-matching procedure to the force data obtained from an explicit atomistic MD simulation of the biomolecular system of interest. Hence, the method is termed a "multiscale" CG (MS-CG) approach in which explicit atomistic-level forces are propagated upward in scale to the coarse-grained level. The CG sites in the lipid bilayer application were associated with the centers-of-mass of atomic groups because of the simplicity in the evaluation of the forces acting on them from the atomistic data. The resulting CG lipid bilayer model is shown to accurately reproduce the structural properties of the phospholipid bilayer.     

Computer program for finding all possible cycles in graphs

A new approach is presented for identifying all possible cycles in graphs. Input data are the total numbers of vertices and edges, as well as the vertex adjacencies using arbitrary vertex numbering. A homeomorphically reduced graph (HRG) is constructed by ignoring vertices of degree less than three. The algorithm is based on successive generation of possible edge-combinations in the HRG. If a combination yields a cycle, it is either printed or stored and then finally printed in a list of all possible cycles arranged in the order of increasing ring size. A unique numbering of the cycle is used. The computer program is listed and exemplified. Computing times are given.     

A new method for estimation of viscosity-average molecular weight of polymers from GPC

A novel approach for the determination of the viscosity-average molecular weight of polymers for which Mark–Houwink constants are not known is presented. This method can be applied to narrow as well as broad-molecular-weight-distribution polymer samples and requires only the GPC chromatogram and viscosity data. The proposed method was tested using polystyrene and poly(methyl methacrylate) in toluene and THF. Molecular weights computed by the proposed approach are in good agreement with those obtained using conventional techniques.     

Prediction of transmembrane proteins based on the continuous wavelet transform

A novel method based on continuous wavelet transform (CWT) for predicting the number and location of helices in membrane proteins is presented. Two bacteria proteins are chosen as examples to describe the prediction of transmembrane helices (HTM) by using this method. Selections of an appropriate dilation and hydrophobicity data types are discussed in the text. The results indicate that CWT is a promising approach for the prediction of HTM.     

Automation and Standardization—A Coupled Approach towards Reproducible Sample Preparation Protocols for Nanomaterial Analysis

Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by automation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been developed, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.     

Multivariate statistical analysis of a multi-step industrial processes

Monitoring and quality control of industrial processes often produce information on how the data have been obtained. In batch processes, for instance, the process is carried out in stages; some process or control parameters are set at each stage. However, the obtained data might not be utilized efficiently, even if this information may reveal significant knowledge about process dynamics or ongoing phenomena. When studying the process data, it may be important to analyse the data in the light of the physical or time-wise development of each process step. In this paper, a unified approach to analyse multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors. This approach will show how the process develops from a data point of view. The procedure is illustrated on a relatively simple industrial batch process, but it is also applicable in a general context, where knowledge about the variables is available.     

Synthesis of hydrophilic polymer-grafted ultrafine inorganic oxide particles in protic media at ambient temperature via atom transfer radical polymerization: use of an electrostatically adsorbed polyelectrolytic macroinitiator

A new approach for the surface grafting of polymer chains to colloidal substrates is described. A cationic macroinitiator has been designed for the surface polymerization of a wide range ofhydrophilic methacrylates from ultrafine inorganic oxide sols by atom transfer radical polymerization in protic media at ambient temperature. One advantage of this approach is that it allows one-pot syntheses: the macroinitiator is adsorbed onto the sol, followed by an in situ polymerization. Nonionic, cationic, and betaine monomers can be polymerized directly by this protocol, with reasonably high conversions being obtained, as judged by 1H NMR spectroscopy. Anionic monomers such as sodium 4-styrenesulfonate cannot be polymerized directly due to incompatibility problems with the cationic macroinitiator-coated sol. However, hydroxylated monomers such as glycerol monomethacrylate can be surface-polymerized and then converted to anionic polyelectrolytes by reaction with succinic anhydride under mild conditions. This derivatization was confirmed by diffuse reflectance infrared Fourier transform (DRIFT) spectroscopic analysis. Thermogravimetry was used to assess the degree of polymer grafting. Higher target degrees of polymerization led to increased grafted polymer loadings, as expected. Particle morphologies and relative degrees of dispersion in aqueous solution were assessed by transmission electron microscopy and dynamic light scattering, respectively. Surface characterization of the polymer-grafted sols was achieved by X-ray photoelectron spectroscopy and aqueous electrophoresis measurements. Most of the data reported in this study concern surface polymerizations from ultrafine silica sols, but some preliminary data for ultrafine tin(IV) oxide sols are also presented. Since most surfaces are negatively charged, this cationic macroinitiator approach can, in principle, be extended to include a wide range of sols, latexes, and planar substrates without requiring a separate surface functionalization step.     

CFD-based atmospheric dispersion modeling in real urban environments

The process of CFD model application for atmospheric dispersion modeling is presented. Increasing the CPU power opens new possibilities of the CFD approach application for consequence analysis in real complex urban environments. As successful CFD simulation is directly dependent on the quality and complexity of the computational mesh, a new methodology of transferring the Geographic Information System (GIS) data to the computational mesh can be utilized. A user software for importing and manipulation with the GIS data and their subsequent transfer to an instructional file for the generation of the computational mesh was prepared and tested. The introduced methodology is relatively simple and it requires only a small amount of input data. The process of creating a computational mesh is very straightforward and fast, which enables the application of CFD modeling in urban environments in all fields of engineering applications in safety analysis. Several recommendations concerning proper definition of boundary conditions for atmospheric dispersion modeling were summarized. The presented approach was tested on a realistic case study of liquefied chlorine release in a real town. Results obtained by the CFD approach were compared with those obtained by a simpler but standard integral model.     

A comparison of the analysis of covariance (ANCOVA) and range-based approaches for assessing batch-to-batch variability of the stability of pharmaceutical products

Stability data were generated by the Monte Carlo method, and batch-to-batch variability was evaluated by analysis of differences in slope and intercept according to the analysis of covariance (ANCOVA) approach recommended in the FDA Guidance. Using the same generated data, batch-to-batch variability was also evaluated by assessing the equivalence of shelf lives estimated for individual batches based on the range (Range-based approach) in order to compare the ability of the two approaches to detect stability differences among batches. The results of the study indicated that the Range-based approach can detect a 30% difference in the slope of degradation curves among batches with a similar beta error as the ANCOVA approach, provided that degradation data are obtained with assay errors below 0.5. The range-based approach appears to be useful as an alternative method to ANCOVA, if it is modified such that the variance of estimates is taken into account.     

Modeling fluid diffusion using the lattice density functional theory approach: counterdiffusion in an external field

The dependence of the diffusivity on temperature, pressure, and composition is not understood well; consequently, data is preferred significantly over correlations in most practical situations. Even in dilute gases, the contributions of attractions and repulsions to the diffusivity are difficult to understand on a molecular level without performing simulations. We have developed a Lattice Density Functional Theory (LDFT) approach for modeling diffusion to supplement existing methods that are very rigorous but computationally demanding. The LDFT approach is analogous to the van der Waals equation in how it accounts for molecular interactions in that it has first-order corrections to ideal behavior; it is an extension of the Equilibrium LDFT for adsorption and phase behavior. In this work, the LDFT approach is presented and demonstrated by modeling the problem of color counterdiffusion in an externally-applied potential field. This potential field, in combination with the intermolecular potential function, creates a diffusion regime in which repulsions cause oscillations in the density profile. Using the LDFT approach, the oscillations were described and attributed to nearest-neighbor and next nearest-neighbor interactions. The LDFT approach gives qualitative and quantitative agreement with dual control-volume Grand Canonical Molecular Dynamics simulations.     

Accounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum images

Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by ‘counting statistics’ and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time‐of‐flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis. Published in 2004 by John Wiley & Sons, Ltd.     

Stochastic simulations of polymer growth and isomerization in the polymerization of propylene catalyzed by Pd-based diimine catalysts

A model is presented that employs a stochastic approach to the simulation of polyolefin chain growth and isomerization. The model is applied to propylene polymerization catalyzed by Pd-based diimine catalysts. The stochastic approach links the microscopic (quantum chemical) approach with modeling of the macroscopic systems. The DFT calculated energies of the elementary reactions and their barriers have been used as input parameters for the simulations. The influence of the catalyst's steric bulk, as well as polymerization temperature and olefin pressure on the polymer branching and its microstructure, is discussed. The results are in good agreement with available experimental data. In the propylene polymerization catalyzed by Pd(II) complexes with methyl backbone- and -Ph-(i)Pr(2) imine substituents a number of branches of 238 branches/1000 C have been obtained. An increase in polymerization temperature leads to a decrease in the number of branches. Change in olefin pressure does not affect the global number of branches, while it strongly affects the polymer microstructure, leading to hyperbranched structures at low pressures. Further, the simulations confirm the experimental interpretation of the mechanistic details for this process: (1) both 1,2- and 2,1-insertion happen with the ratio of ca. 7:3; (2) there are no insertions at the secondary carbons; and (3) most of the 2,1-insertions are followed by a chain straightening isomerization. Thus, for this catalyst the total number of branches is controlled exclusively by the 1,2-/2,1-insertion ratio. For the catalysts with different substituents the branching can be controlled by a 1,2-/2,1-insertion ratio as well as the fraction of the insertions at the secondary carbons. The results of the present studies demonstrate that a stochastic approach can be successfully used to model the polyolefin microstructures and their catalyst, temperature, and pressure dependence. Further, it can also facilitate interpretation of the experimental results, and can be used to draw general conclusions about the influence of the specific elementary reaction barriers on the polymer structures; this can be helpful for a rational design of the catalysts producing a desired microstructure.     

Variable alignment of high resolution data by cluster analysis

High resolution time-of-flight secondary ion mass spectrometry (HR TOF-SIMS) is a powerful surface analytical method. For complex samples, this technique may yield intricate spectra that are difficult to interpret visually. Chemometric methods are useful for data analysis. However, these methods require that spectra are represented in a matrix format. Variances in mass measurements caused by calibration or instrumental effects may present difficulties in properly aligning mass spectral peaks into the correct columns of the data matrix. Cluster analysis of resolution elements is proposed as an alternative approach to construct the data matrix. An automated method for optimizing the data alignment is presented and evaluated for standard steel samples.