Computer perception of topological symmetry by all-paths algorithm

During the development of our ESESOC system (Expert System for the Elucidation of the Structures of Organic Compounds), computer perception of topological symmetry is essential in searching for the canonical description of a molecular structure, removing the irredundant connections in the structure generation process, and specifying the number of peaks in C-13- and H-1-NMR spectra in the structure evaluation process. In the present paper, a new path identifier is introduced and an algorithm for detection of topological symmetry from a connection table is developed by the all-paths method. (C) 1999 Elsevier Science B.V. All rights reserved.

APPLICATION OF FACTOR-ANALYSIS TO CORRECTION OF SPECTRAL OVERLAP INTERFERENCE IN INDUCTIVELY COUPLED PLASMA ATOMIC EMISSION-SPECTROMETRY

Correction of spectral overlap interference in inductively coupled plasma atomic emission spectrometry by factor analysis is attempted. For the spectral overlap of two known lines, a data matrix can be composed from one or two pure spectra and a spectrum of the mixture. The data matrix is decomposed into a spectra matrix and a concentration matrix by target transformation factor analysis. The component concentration of interest in a binary mixture is obtained from the concentration matrix and interference from the other component is eliminated. This method is applied to correcting spectral interference of yttrium on the determination of copper and aluminium: satisfactory results are obtained. This method may also be applied to correcting spectral overlap interference for more than two lines. Like other methods of correcting spectral interferences, factor analysis can only be used for additive spectral overlap. Results obtained from measurements on copper/yttrium mixtures with different white noise added show that random errors in measurement data do not significantly affect the results of the correction method.

SEARCH FOR THE BASIC REGULARITIES OF THE TRANSITION EMISSION OF EUROPIUM(II) ION IN COMPLEX FLUORIDES USING PATTERN-RECOGNITION

The relationship between structures of complex fluorides and spectral structure of Eu(II) ion in complex fluorides (AB(m)F(n)) is investigated by means of pattern recognition methods, such as KNN, ALKNN, BAYES, LLM, SIMCA and PCA. A learning set consisting of 32 f-f transition emission host compounds and 31 d-f transition emission host compounds and a test set consisting of 27 host compounds were characterized by 12 crystal structural parameters. These parameters, i.e. features, were reduced from 12 to 6 by multiple criteria for the classification of these host compounds as f-f transition emission or d-f transition emission. A recognition rate from 79.4 to 96.8% and prediction capabilities from 85.2 to 92.6% were obtained. According to the above results, the spectral structures of Eu(II) ion in seven unknown host lattices were predicted.

Deflation based nonlinear canonical correlation analysis

This paper introduces two new techniques for determining nonlinear canonical correlation coefficients between two variable sets. A genetic strategy is incorporated to determine these coefficients. Compared to existing methods for nonlinear canonical correlation analysis (NLCCA), the benefits here are that the nonlinear mapping requires fewer parameters to be determined, consequently a more parsimonious NLCCA model can be established which is therefore simpler to interpret. A further contribution of the paper is the investigation of a variety of nonlinear deflation procedures for determining the subsequent nonlinear canonical coefficients. The benefits of the new approaches presented are demonstrated by application to an example from the literature and to recorded data from an industrial melter process. These studies show the advantages of the new NLCCA techniques presented and suggest that a nonlinear deflation procedure should be considered. (c) 2006 Elsevier B.V. All rights reserved.

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

The application of NMR to study changes in polar metabolite concentrations in beef longissimus dorsi stored for different periods post mortem

In this study data generated by H-1 NMR spectroscopy were combined with chemometrics to analyse beef samples aged over a 21 day period. In particular, the amino acids, of which 12 were identified were found to increase over the ageing period with samples matured for 3 days having notably lower concentrations than carcasses aged for 21 days. This is believed to be a result of increased proteolysis within the muscle. This novel approach of using high resolution NMR spectrometry to analyse beef samples has not previously been reported and these findings demonstrate the potential of this technique linked with HPLC to be used as a suitable method for profiling meat samples.

Oligomerisation of linear 1-olefins using a chlorogallate(III) ionic liquid

A Lewis acidic chlorogallate(III) ionic liquid, 1-ethyl-3-methylimidazolium hepta-chlorodigallate(III), [C(2)mim][Ga2Cl7], was successfully used to oligomerise 1-pentene. The influence of temperature, time, catalyst concentration, and stirring rate on conversion and product distribution was modelled using a design of experiment (DoE) approach (chemometrics). The process was optimised for lubricant base oils production; the C20-C50 fraction (where Cn indicates the number of carbons in the oligomer) was maximised, while the heavier oligomer fraction (>C50) was minimised.

Investigation of the Human Brain Metabolome to Identify Potential Markers for Early Diagnosis and Therapeutic Targets of Alzheimer’s Disease

A study combining high resolution mass spectrometry (liquid chromatography-quadrupole time-of-flight-mass spectrometry, UPLC-QTof-MS) and chemometrics for the analysis of post-mortem brain tissue from subjects with Alzheimer’s disease (AD) (n = 15) and healthy age-matched controls (n = 15) was undertaken. The huge potential of this metabolomics approach for distinguishing AD cases is underlined by the correct prediction of disease status in 94–97% of cases. Predictive power was confirmed in a blind test set of 60 samples, reaching 100% diagnostic accuracy. The approach also indicated compounds significantly altered in concentration following the onset of human AD. Using orthogonal partial least-squares discriminant analysis (OPLS-DA), a multivariate model was created for both modes of acquisition explaining the maximum amount of variation between sample groups (Positive Mode-R2 = 97%; Q2 = 93%; root mean squared error of validation (RMSEV) = 13%; Negative Mode-R2 = 99%; Q2 = 92%; RMSEV = 15%). In brain extracts, 1264 and 1457 ions of interest were detected for the different modes of acquisition (positive and negative, respectively). Incorporation of gender into the model increased predictive accuracy and decreased RMSEV values. High resolution UPLC-QTof-MS has not previously been employed to biochemically profile post-mortem brain tissue, and the novel methods described and validated herein prove its potential for making new discoveries related to the etiology, pathophysiology, and treatment of degenerative brain disorders.

The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and
have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of
2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine
and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics
to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients
of determination (R2) were found to be 0.89–0.99 depending on the mathematical algorithm used,
the data pre-processing applied and the sample type used. The corresponding values for the root mean
square error of calibration and prediction were found to be 0.081–0.276% and 0.134–0.368%, respectively,
again depending on the chemometric treatment applied to the data and sample type. In addition, adopting
a qualitative approach with the spectral data and applying PCA, it was possible to discriminate
between the four samples types and also, by generation of Cooman’s plots, possible to distinguish
between adulterated and non-adulterated samples.