Information content of data and variables and types of weighting in least-squares regression methods

Algorithms are given for evaluating the relative amount of useful information related to a particular parameter which is carried by individual data points and intervals of the variables. The algorithms provide an efficient means of using the information contained in a set of data. Applications to the optimization of weighting in regression methods are described. Several informational and combined informational-statistical types of weighting are studied as a means of improving the accuracy and precision of the parameters obtained by non-linear regression.     

Using mixture models for bump-hunting in the results of proficiency tests

The interpretation of the results of proficiency tests by the use of mixture models is described. The data are interpreted as a sample from a mixture of several normal populations. The calculation of the statistics (the means, variances and proportions of each component) is accomplished by means of the ‘EM’ algorithm. The method has several advantages over those previously advanced, principally that the algorithm is fast and easy to execute. Examples from proficiency testing are discussed.     

Inorganic-Organic Cross-Linking in UV Curable Hard Coats Based Upon Vinyltriethoxysilane-Tetraethoxysilane-Polyfunctional Acrylate Hybrid Polymers: A Raman Spectroscopic Study

Hydrolysis, polycondensation and UV-induced radical polymerization processes in binary and ternary mixtures of vinyltriethoxysilane (VTEOS), tetraethoxysilane (TEOS), and several polyfunctional acrylates as reactive diluents were studied by means of FT-Raman and confocal micro-Raman spectroscopy. The study comprises measurements on sols, gels, xerogels, and thin films applied on top of glass slides and polymeric substrates. Characteristic Raman bands are utilized to gain information about the structural evolution, inorganic network connectivity, and organic cross-linking reactions. Supplementary, ²⁹Si-NMR spectroscopic data are considered and correlated with Raman data. Structure-property correlations based on spectroscopic and mechanical data are outlined and discussed. It is demonstrated that thin hybrid polymer films may be studied in-situ by means of confocal micro-Raman spectroscopy.     

A review of electron-capture and electron-transfer dissociation tandem mass spectrometry in polymer chemistry

Mass spectrometry (MS)-based studies of synthetic polymers often characterise detected polymer components using mass data alone. However when mass-based characterisations are ambiguous, tandem MS (MS/MS) offers a means by which additional analytical information may be collected. This review provides a synopsis of two particularly promising methods of dissociating polymer ions during MS/MS: electron-capture and electron-transfer dissociation (ECD and ETD, respectively). The article opens with a summary of the basic characteristics and operating principles of ECD and ETD, and relates these techniques to other methods of dissociating gas-phase ions, such as collision-induced dissociation (CID). Insights into ECD- and ETD-based MS/MS, gained from studies into proteins and peptides, are then discussed in relation to polymer chemistry. Finally, ECD- and ETD-based studies into various classes of polymer are summarised; for each polymer class, ECD- and ETD-derived data are compared to CID-derived data. These discussions identify ECD and ETD as powerful means by which unique and diagnostically useful polymer ion fragmentation data may be generated, and techniques worthy of increased utilisation by the polymer chemistry community.     

Data modelling with neural networks: Advantages and limitations

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.     

Structure of SDS micelles with propylene carbonate as cosolvent: a PGSE-NMR and SAXS study

The effect of propylene carbonate on SDS micelles was investigated by means of pulsed gradient spin-echo (PGSE) NMR, small-angle X-ray scattering (SAXS), conductivity and ion-selective electrode (ISE) measurements. The knowledge of the cosolvent partition between continuous phase and micelles (obtained by means of PGSE-NMR) allowed the identification of relevant dilution paths. Along these paths the system is composed of identical micelles that become more and more diluted. The extrapolation of measured self-diffusion coefficient to infinite dilution (where direct and hydrodynamic interactions are negligible) permits the determination of hydrodynamic size of the micelles. Moreover, the micelle ionization degree (measured by means of ISE) combined with PGSE-NMR and conductivity data furnishes an estimate of the aggregation number without any assumptions on micellar shape. On the other hand, troublesome hydrodynamic interactions are irrelevant to SAXS, and scattering data collected at fixed composition can be analyzed according to a reasonable model by exploiting the insight on the propylene carbonate partition gained through PGSE-NMR. By means of these approaches, we have found that propylene carbonate acts mainly as cosurfactant for the SDS micelles, decreasing their size and aggregation number by increasing the mean headgroup area of SDS.     

River Pollution Data Interpreted by Means of Chemometric Methods

Environmental data, including river pollution data, are characterized by high variability. Much information is lost by using only univariate graphical or statistical methods for data evaluation and interpretation. Chemometric methods, in particular methods of multivariate data analysis, help to extract the latent information in such data. The combination of cluster analysis as the first step and multivariate analysis of variance and discriminant analysis as the second step enables identification of similar locations in a river. Pollution sources and dischargers can be detected by means of factor analysis. The deposition–remobilization behavior of metals in a river can be described using partial least squares regression. Summarizing, it can be stated that methods of multivariate data analysis are powerful tools for the evaluation and interpretation of river pollution data.     

The darcpluridata system: the 13C-n.m.r. data bank1

The capabilities of the DARC system are discussed and illustrated by the storage and retrieval functions of the ¹³C-n-m.r. data bank of the DARC PLURIDATA system. The data covered by the bank, as well as the input stream to the bank and validation of the spectra, are described. Particular stress is laid on the DARC structural retrieval system, which illustrates the interactive interrogation of a chemical data bank by means of the structural diagram of a molecule, i.e. the universal language in chemistry. The potential of the ¹³C-n.m.r. data bank in computer-aided structural elucidation is outlined.     

A chemometric approach based on a novel similarity/diversity measure for the characterisation and selection of electronic nose sensors

Electronic nose sensor signals provide a digital fingerprint of the product in analysis, which can be subsequently investigated by means of chemometrics. In this paper, the fingerprint characterisation of electronic nose data has been studied by means of a novel chemometric approach based on the partial ordering technique and the Hasse matrix. This matrix can be associated to each data sequence and the similarity between two sequences can be evaluated with the definition of a distance between the corresponding Hasse matrices. Since all the signals achieved along time are intrinsically ordered, the data provided by electronic nose can be also considered as sequential data and consequently characterized by means of the proposed approach. The similarity/diversity measure has been here applied in order to characterize the class discrimination capability of each electronic nose sensor: extra virgin olive oil samples of different geographical origin have been considered and Hasse distances have been used to select the sensors which appear more able to discriminate the olive oil origins. The distance based on the Hasse matrix has showed some useful properties and proved to be able to link each electronic nose time profile to a meaningful mathematical term (the Hasse matrix), which can be consequently studied by multivariate analysis.     

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.     

Spectroscopic and structural elucidation of L-tyrosine-containing dipeptides valyl-tyrosine and tyrosyl-alanine: solid-state IR-LD spectroscopy, quantum chemical calculations and vibrational analysis

L-Tyrosine-containing dipeptides valyl-tyrosine (H-Val-Tyr-OH) and tyrosyl-alanine (H-Tyr-Ala-OH) are characterized structurally by means of quantum chemical ab initio calculations and solid-state linear-dichroic infrared (IR-LD) spectroscopy. The IR-characteristic bands are assigned by application of reducing-difference procedure for polarized IR-spectra interpretation. Infrared data obtained are supported as well by the made vibrational analysis. The structures of both peptides are predicted on the basis of conformational analysis and structural information, obtained by the shown IR-spectroscopic tool.     

Simultaneous thermal analysis/mass spectrometric investigations on the thermal behaviour of noble metal complexes

The results of investigations on the thermal behaviour of three selected noble metal complexes, performed by means of simultaneous thermal analysis/mass spectrometry are discussed. The thermal analysis data, such as the initial temperatures for the beginning of the weight loss, the amounts of the weight loss, as well as the characteristic DTG and DTA temperatures, are tabulated and the simultaneously recorded electron impact mass spectra are interpreted. The temperature-dependent formation and cleavage of typical complex-structure-related substituents is described and established by the evaluation of the course of the corresponding ion-current intensities.     

Solid-state structural properties of 2,4,6-trimethoxybenzene derivatives, determined directly from powder X-ray diffraction data in conjunction with other techniques

Structural properties of 2,4,6-trimethoxybenzaldehyde, 2,4,6-trimethoxybenzyl alcohol and 2,4,6-trimethoxyacetophenone have been determined directly from powder X-ray diffraction data, using the direct-space Genetic Algorithm (GA) technique for structure solution followed by Rietveld refinement. Structural similarities and contrasts within this family of materials are elucidated. The work illustrates the value of utilizing information from other sources, including spectroscopic data and computational techniques, as a means of augmenting the structural knowledge established from the powder X-ray diffraction data.     

Linear-dichroic infrared spectral (IR-LD) analysis of codeine and its derivatives

By means of IR-LD spectroscopy of oriented as suspension in nematic liquid crystal solids, a detailed IR-bands assignment and a stereo-structural prediction of codeine, codeinone and N-norcodeine have been carried out. The data are compared with known crystallographic ones of codeine and codeinone obtained by single crystal X-ray diffraction.     

Superstructure of EU4CD25: a quasicrystal approximant

The quasicrystal approximant Eu(4)Cd(25), formerly designated EuCd(6), was synthesized and characterized by means of single-crystal X-ray diffraction. Superstructure reflections corresponding to an F-centered cubic unit cell with a doubled cell-parameter relative to the I-centered cubic sub-cell could be observed in the diffraction data. This gives the largest cubic unit cell reported to date among binary alloys. The superstructure exhibits structural features that are found in all the RE-Cd(6) approximants as well as a further ordering between vacant/occupied Cd(8) cubes and oriented Cd(4) tetrahedra, which causes the superstructure. A reversible high-temperature phase-transition was observed at 782 K by means of DSC.     

Simulation of the Long Term Behaviour of Plastics Components

Summary: This paper presents a method to model the mechanical behavior of polymers over a wide time- and load-range by means of finite element analyses. The method includes a material model as well as the determination of material parameters to calibrate the material model. As a special feature of this method the model is calibrated only by using creep data that are commonly available in material data bases. So the procedure improves the simulation of the long time behavior of plastic-components without an additional experimental effort. In combination with time-temperature-superposition principle, the temperature dependency of the long term behavior is represented, too. The simulation results are validated by creep experiments on an example part.     

Principal component analysis application in polycyclic aromatic hydrocarbons “mussel watch” analyses for source identification

This article aims to show how a careful pre-treatment of data can be used to demonstrate various features embedded in a given data set obtained from a “mussel watch” survey, namely site- and source-specific characteristics and weather-related changes, and to provide indications so as to allow comparison with analyses performed on another substrate matrix. Polycyclic aromatic hydrocarbons (PAHs) biomonitored in the aquatic environment by means of caged mussels are compared by site and by season. Moreover, their fingerprints were compared to marine sediments and atmospheric airborne PAHs. The characterization of the sampling stations by means of the multivariate technique called principal component analysis (PCA) allows distinguishing the prevalence of pyrogenic or petrogenic types of pollution and between two kinds of combustibles. This was confirmed by jointly analyzing the percent composition of sea (mussel) and air (filter) samples.     

A complete 1H and 13C NMR data assignment for 3-phenylmethylene-1H,3H-naphtho-[1,8-c,d]-pyran-1-one

Complete assignments of the 1H and 13C NMR chemical shifts for 3-phenylmethylene-1H,3H-naphtho-[1,8-c,d]-pyran-1-one were done by means of one- and two-dimensional NMR techniques, including 1H-(1)H COSY, HMQC and HMBC spectra. Ab initio quantum chemistry calculations and a shift prediction by an incremental method provided values close to the proposed assignments. All mid-IR spectral bands are given as reference data. The DRIFT FTIR, ATR FTIR and Raman spectra are given as a Supplementary data in JCAMP-DX format, version 4.24. In addition, a method of compound's synthesis, that has the product yield higher as compared to already known data in the literature, is given.