Soft or hard ionization of molecules in helium nanodroplets? An electron impact investigation of alcohols and ethers

Electron impact (70 eV) mass spectra of a series of C1-C6 alcohols encased in large superfluid liquid helium nanodroplets (approximately 60,000 helium atoms) have been recorded. The presence of helium alters the fragmentation patterns when compared with the gas phase, with some ion product channels being more strongly affected than others, most notably cleavage of the C(alpha)-H bond in the parent ion to form the corresponding oxonium ion. Parent ion intensities are also enhanced by the helium, but only for the two cyclic alcohols studied, cyclopentanol and cyclohexanol, is this effect large enough to transform the parent ion from a minor product (in the gas phase) into the most abundant ion in the helium droplet experiments. To demonstrate that these findings are not unique to alcohols, we have also investigated several ethers. The results obtained for both alcohols and ethers are difficult to explain solely by rapid cooling of the excited parent ions by the surrounding superfluid helium, although this undoubtedly takes place. A second factor also seems to be involved, a cage effect which favors hydrogen atom loss over other fragmentation channels. The set of molecules explored in this work suggest that electron impact ionization of doped helium nanodroplets does not provide a sufficiently large softening effect to be useful in analytical mass spectrometry.     

Pattern recognition for the analysis of polymeric materials

A new method of polymer classification is described involving dynamic mechanical analysis of polymer properties as temperature is changed. The method is based on the chemometric analysis of the damping factor (tan delta) as a function of temperature. In this study four polymer groups, namely, polypropylene, low density polyethylene, polystyrene and acrylonitrile-butadiene-styrene, each characterised by different grades, were studied. The aim is to distinguish polymer groups from each other. The polymers were studied over a temperature range of -50 degrees C until the minimum stiffness was reached, tan delta values were recorded approximately every 1.5 degrees . Principal components analysis was performed to visualise groupings and also for feature reduction prior to classification and clustering. Several clustering and classification methods were compared including k-means clustering, hierarchical cluster analysis, linear discriminant analysis, k-nearest neighbours, and class distances using both Euclidean and Mahalanobis measures. It is demonstrated that thermal analysis together with chemometrics provides excellent discrimination, representing a new approach for characterisation of polymers.     

Chemometric Methods for the Study of Toxic Metals on the Growth of Plants: Use of Experimental Design and Response Surface Methodology

Abstract

Chemometrics is a recent discipline concerned, principally, with the application of mathematics and statistics to laboratory systems. One way in which the chemometrician can aid the environmental analytical chemist is via planned experimental designs. In this paper the importance of experimental design is illustrated and the main considerations prior to experimentation, namely, degrees of freedom, analytical errors, coding and modelling, are outlined. This is exemplified by a study of the influence of potentially toxic heavy metals on the growth of barley seedlings. Undesigned univariate experiments suggest that Tl is probably more toxic than Cd. A three factor central composite design is reported, to study the relative toxicities of Tl, Cd and Pb and also of Tl, Fe and Zn. The paper exemplifies how much information can be obtained from the resultant experimental response data. Multilinear regression can be employed to produce a quadratic model: this can be interpreted graphically by reconstructed univariate response curves and 3-dimensional response surfaces. Analysis of variance is a statistical method for computing how well the model has been fitted, taking into account analytical errors. With the aid of modern graphical computing, a variety of confidence intervals can be displayed for both univariate and bivariate responses. The usefulness of the design can be visualised by displaying leverage over and outside the experimental region. Finally future trends in multivariate response methodology are discussed.     

Electron impact ionization of haloalkanes in helium nanodroplets

Electron impact (EI) mass spectra of a selection of C1-C3 haloalkanes in helium nanodroplets have been recorded to determine if the helium solvent can significantly reduce molecular ion fragmentation. Haloalkanes were chosen for investigation because their EI mass spectra in the gas phase show extensive ion fragmentation. There is no evidence of any major softening effect in large helium droplets ( approximately 60 000 helium atoms), but some branching ratios are altered. In particular, channels requiring C-C bond fission or concerted processes leading to the ejection of hydrogen halide molecules are suppressed by helium solvation. Rapid cooling by the helium is not sufficient to account for all the differences between the helium droplet and gas phase mass spectra. It is also suggested that the formation of a solid "snowball" of helium around the molecular ion introduces a cage effect, which enhances those fragmentation channels that require minimal disruption to the helium cage for products to escape.     

Application of multivariate curve resolution methods to on-flow LC-NMR

The application of evolving window factor analysis (EFA), subwindow factor analysis (SFA), iterative target transformation factor analysis (ITTFA), alternating least squares (ALS), Gentle, automatic window factor analysis (AUTOWFA) and constrained key variable regression (CKVR) to resolve on-flow LC-NMR data of eight compounds into individual concentration and spectral profiles is described. CKVR has been reviewed critically and modifications are suggested to obtain improved results. A comparison is made between three single variable selection methods namely, orthogonal projection approach (OPA), simple-to-use interactive self-modelling mixture analysis approach (SIMPLISMA) and simplified Borgen method (SBM). It is demonstrated that LC-NMR data can be resolved if NMR peak cluster information is utilised.     

Efficient Simulation of Markov Chains Using Segmentation

A methodology is proposed that is suitable for efficient simulation of continuous-time Markov chains that are nearly-completely decomposable. For such Markov chains the effort to adequately explore the state space via Crude Monte Carlo (CMC) simulation can be extremely large. The purpose of this paper is to provide a fast alternative to the standard CMC algorithm, which we call Aggregate Monte Carlo (AMC). The idea of the AMC algorithm is to reduce the jumping back and forth of the Markov chain in small subregions of the state space. We accomplish this by aggregating such problem regions into single states. We discuss two methods to identify collections of states where the Markov chain may become ‘trapped’: the stochastic watershed segmentation from image analysis, and a graph-theoretic decomposition method. As a motivating application, we consider the problem of estimating the charge carrier mobility of disordered organic semiconductors, which contain low-energy regions in which the charge carrier can quickly become stuck. It is shown that the AMC estimator for the charge carrier mobility reduces computational costs by several orders of magnitude compared to the CMC estimator.     

Discrimination between tablet production methods using pyrolysis-gas chromatography-mass spectrometry and pattern recognition

Wet granulation and direct compression are two processes employed in tablet preparation. In this paper, pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) is used to discriminate these processes with the help of chemometric techniques. The data analysis procedure is as follows. First, deconvolute the Py-GC-MS data of each sample into concentration profiles and spectra, and then construct a matrix with each compound corresponding to one column; those contained only in a small number of samples are then removed. Second, the main principal components are kept after excluding three variables and one sample, and further processed by Fisher discriminant analysis. Third, the resultant data are assigned to classes using unsupervised and supervised classification methods. Results from cross-validation show that only 3 of 20 samples are misclassified by the Mahalanobis distance measure.     

Self-Organizing Maps and Support Vector Regression as aids to coupled chromatography: illustrated by predicting spoilage in apples using volatile organic compounds

The paper describes the application of SOMs (Self-Organizing Maps) and SVR (Support Vector Regression) to pattern recognition in GC-MS (gas chromatography-mass spectrometry). The data are applied to two groups of apples, one which is a control and one which has been inoculated with Penicillium expansum and which becomes spoiled over the 10-day period of the experiment. GC-MS of SPME (solid phase microextraction) samples of volatiles from these apples were recorded, on replicate samples, over time, to give 58 samples used for pattern recognition and a peak table obtained. A new approach for finding the optimum SVR parameters called differential evolution is described. SOMs are presented in the form of two-dimensional maps. This paper shows the potential of using machine learning methods for pattern recognition in analytical chemistry, particularly as applied to food chemistry and biology where trends are likely to be non-linear.