Ulf Edlund

Multivariate Data Analysis in Chemistry

Any data table produced in a chemical investigation can be analysed by bilinear projection methods, i. e. principal components and factor analysis and their extensions. Representing the table rows (objects) as points in a p-dimensional space, these methods project the point swarm of the data set or parts of it down on a F-dimensional subspace (plane or hyperplane). Different questions put to the data table correspond to different projections.

Four levels of pattern recognition

Abstract Problems of pattern recognition in chemistry and other subjects can be divided conveniently into four different types depending on the level of scope of the problem. (1) Classification into one of a number of defined classes. As an example blood samples taken from persons known to be either controls or welders are considered. The problem is whether trace element concentrations in these samples contain information on whether or not a person is a welder. (2) Level 1 plus the possibility that an object is an outlier, i.e. does not belong to any of the defined classes. As an example, the use of 13C-n.m.r. data to decide whether 2-substituted norbornanes have the exo or endo structure is discussed. (2A) Level 2, asymmetric. This situation occurs when one class does not have a systematic structure, but another class is homogeneous and can be described by a level 2 model. This occurs in the classification of materials or compounds as good or bad, active or inactive, and in binary classifications. As an example the use of trace element data to classify steel samples as having good or poor properties of strength is discussed. (3) Level 2 plus the ability to relate the variables measured to external properties of continuous character. As an example, the classification of a series of chemical compounds as β -receptor blockers, β -receptor stimulants, or neither, on the basis of their structural variables is discussed. In addition, relations between these structural variables and the measured biological activity are sought within each of the two classes. (4) Level 3 with the difference that several external property variables in the objects are measured. It may be desirable to use variables of the objects both for classification and for relations to several property variables: such examples are numerous in analytical chemistry.

Pectin Methyl Esterase Inhibits Intrusive and Symplastic Cell Growth in Developing Wood Cells of Populus

Wood cells, unlike most other cells in plants, grow by a unique combination of intrusive and symplastic growth. Fibers grow in diameter by diffuse symplastic growth, but they elongate solely by intrusive apical growth penetrating the pectin-rich middle lamella that cements neighboring cells together. In contrast, vessel elements grow in diameter by a combination of intrusive and symplastic growth. We demonstrate that an abundant pectin methyl esterase (PME; EC 3.1.1.11) from wood-forming tissues of hybrid aspen (Populus tremula × tremuloides) acts as a negative regulator of both symplastic and intrusive growth of developing wood cells. When PttPME1 expression was up- and down-regulated in transgenic aspen trees, the PME activity in wood-forming tissues was correspondingly altered. PME removes methyl ester groups from homogalacturonan (HG) and transgenic trees had modified HG methylesterification patterns, as demonstrated by two-dimensional nuclear magnetic resonance and immunostaining using PAM1 and LM7 antibodies. In situ distributions of PAM1 and LM7 epitopes revealed changes in pectin methylesterification in transgenic trees that were specifically localized in expanding wood cells. The results show that en block deesterification of HG by PttPME1 inhibits both symplastic growth and intrusive growth. PttPME1 is therefore involved in mechanisms determining fiber width and length in the wood of aspen trees.

Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression

Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative Q2 values. For the PLS models based on untreated NIR spectra the cumulative Q2 values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative Q2 values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC.

NMR and Raman characterization of pressure polymerized C60

Bulk C60 has been treated at 1.1 GPa and 550–585 K, producing a dense insoluble material which on heating to above 600 K reverts to normal C60. Raman and IR studies on modified material show a larg ...

Synthesis, Structure, Spectroscopic Studies, and Complexation of Novel Crown Ether Butadienyl Dyes

Butadienyl dyes of the benzothiazole series with various fragments of benzocrown ethers 1a-c were synthesized for the first time. The structures and spectral properties of crown-containing butadien ...

Analysis of 13C NMR data by means of pattern recognition methodology

A data analysis methodology called pattern recognition (PaRC) and its application to the classification problem in the 13C NMR field is presented. By the classification method used, a separate mathematical model for each class of compounds is calibrated by means of a reference set formed by shift parameters of structures with known framework/assignment. The parameters obtained that define these models are then used to classify “unknown” structures provided that these unknowns are similar to either one of the reference sets. Well-known statistical tests give a measure of how well the unknowns fit a certain class. The methods will also calculate the discrimination power for each variable, that is, which carbons are the most relevant for the classification problem. A simple and convenient alternative or supplement to the sophisticated computer systems is also presented. Both of these classification approaches are illustrated by two examples: (i) 2-substituted norbornanes, and (ii) polychlorinated diphenyl ethers.

Reactivity of dissolving pulp:: characterisation using chemical properties, NMR spectroscopy and multivariate data analysis

The reactivity of dissolving pulp was experimentally determined in termsof residual cellulose in viscose. The correlations between 11 chemicalproperties of pulp and filter values and residual cellulose contents of viscosewere then investigated by multivariate data analysis. Both the viscose filtervalue and the residual cellulose were well modelled from the 11 propertiesby partial least squares regression. The results show that pulps with highacetone extractable fractions, high magnesium contents, low alkali resistanceand low viscosity, gave low viscose filter values and low residual cellulosecontents. Pulps with low residual cellulose contents also had low carboxylgroupcontents and low polydispersity. The results are interpreted as that in pulpwith high reactivity, the hemicellulose content is low and that the cellulosechains are shorter and more soluble in alkali. An explanation of the positiveeffect from the high extractive content is that the extractives facilitate thediffusion of carbon disulfide. A principal component analysis of CP/MAS13C-NMR spectral data of six pulp samples showed that differences inreactivity between the pulps could be explained by variations in the hydrogenbonds in the cellulose and/or changes in the glucosidic bonds. In a separatestudy electron beam processing enhanced the reactivity, i.e. lowered theresidual cellulose content, of the investigated pulps. The magnitude of theelectron dose, within the tested range (5.4–23.7 kGy), didnotseem to be important, but the reactivity within pulp sheets tended to be ratherinhomogeneous.

Interpretation of NMR substituent parameters by the use of a pattern recognition approach

Abstract The SIMCA method of pattern recognition was utilized to classify three sets of 4-substituted styrenes. In addition, parameters derived from the class models (principal component models) were used to investigate the relative sensitivity of various positions to substituent effects. In order to describe the substituent influence on the relevant variables it was shown that a one-component model was statistically sufficient. Moreover, the component values for the compounds were well correlated to 19 F shielding changes in a conjugated system where the 19 F position is remote from the substitution center.

Clustering of aryl carbon-13 nuclear magnetic resonance substituent chemical shifts. A multivariate data analysis using principal components

A principal component analysis of the 13C substituent-induced chemical shifts of 82 monosubstituted benzenes shows that ca. 90% of the substituents belong to one of four clusters, acceptors, alkyls, donors, or halogens. This grouping is confirmed statistically. The extensions of the subclasses are not parallel. It is also shown that the predictive capability of the single-parameter models for each subclass is better than any multiparameter model applied on the whole data set. The observed grouping of substituents provides an explanation to the apparent correlation frequently found between 13C n.m.r. chemical shifts and dual substituent parameters. The ability of the statistical method to discover incorrect shift data is also illustrated.