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Biomedical subjects

Richard G Brereton

Publications and source records attributed to Richard G Brereton.

14 recordsLinked to original sources
PubMed2006

Factors influencing the contamination of UK banknotes with drugs of abuse.

Bank of England banknotes sampled from different locations in the UK have been analysed for the presence of cocaine, diamorphine (DAM), Delta(9)-tetrahydrocannabinol (THC) and 3,4-methylenedioxymethamphetamine (MDMA). A database of the contamination detected is routinely used as a benchmark against which the contamination detected on seized banknotes can be compared. Evidence presented at court details how banknotes seized from a suspect may differ from banknotes in general circulation in terms of their contamination with controlled drugs. A question arising from such evidence is whether seized banknotes could have become contaminated through being in circulation in drug "hot spots". In order to address this issue, a Plackett-Burman experimental design was used to investigate the influence of source location and other factors on banknote contamination with drugs of abuse. Banknotes were drawn from banks in eight regions throughout the UK. Each location could be described by a unique combination of the factors under investigation, namely whether the location was rural or urban, in the North or South of the UK, and whether it was a port of entry. The socio-economic class and the proportion of drug offenders in the area and the denomination of the banknotes were also considered as potentially influential factors. Indices were calculated to describe the degree to which samples were contaminated with different drugs, and normal probability plots were used to identify the factors that could account for the contamination observed. Whilst some factors were more influential than others, it was shown that, at the 95% confidence level, none of the proposed factors were significant influences on the contamination. Cocaine contamination on banknotes has been shown to follow a log-normal distribution. It was, therefore, possible to calculate F- and t-statistics to compare the cocaine contamination on the entire sample set with that detected on a second sample set consisting of banknotes all drawn from a single bank branch. It was shown that both inter-bank samples and intra-bank samples had similar variance and similar contamination levels at the 95% confidence level. This suggests that there are no significant regional trends in the contamination of banknotes with drugs of abuse across the UK.

Journal Article↗
PubMed2006

In situ surface sampling of biological objects and preconcentration of their volatiles for chromatographic analysis.

This report describes a rolling stir bar sampling procedure for volatile organic compounds (VOCs) present on various biological surfaces. In combination with thermal desorption/gas chromatography/mass spectrometry, this analytical technique was initially tested for quantitative profiling of human skin VOCs. It is also applicable to additional hydrophobic surfaces such as agricultural products, plant materials, and bird feathers. Use of embedded internal standards provides highly reproducible and quantitative results for a wide variety of sampled trace components. The samples of collected human skin VOCs and standards were found stable under cool storage conditions for at least 14 days, making this approach suitable for field biological and agricultural studies. Additionally, this methodology appears to have potential for forensic and toxicological investigations, as suggested through the analyses of VOC profiles of the human thumb prints recovered from a nonbiological smooth surface.

Animals↗
PubMed2005

Combined kinetics and iterative target transformation factor analysis for spectroscopic monitoring of reactions.

Obtaining rate constants and concentration profiles from spectroscopy is important in reaction monitoring. In this paper, we combined kinetic equations and Iterative Target Transformation Factor Analysis (ITTFA) to resolve spectroscopic data acquired during the course of a reaction. This approach is based on the fact that ITTFA needs a first guess (test vectors) of the parameters that will be estimated (target vectors). Three methods are compared. In the first, originally proposed by Furusjö and Danielsson, kinetic modelling is only used to provide the initial test vectors for ITTFA. In the second the rate constant used to provide the test vectors is optimised until a best fit is reached. In the third, a guess of the rate constant is used to provide the test vectors to ITTFA. The outcome of ITTFA is then used to fit the kinetic model and obtain a new guess of the rate constant. With this constant new concentration profiles are generated and provided to the ITTFA algorithm as new test vectors, in an iterative manner, minimising the residuals of the predicted dataset, until convergence. The second and third methods are new implementations of ITTFA and are compared to the first, established, method. First order (both one and two step) and second order reactions were simulated and instrumental noise was introduced. An experimental second order reaction was also employed to test the methods.

Algorithms↗
PubMed2005

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.

Materials Testing↗
PubMed2005

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.

Chromatography, Liquid↗
PubMed2005

Component detection weighted index of analogy: similarity recognition on liquid chromatographic mass spectral data for the characterization of route/process specific impurities in pharmaceutical tablets.

Detection and identification of impurities in pharmaceuticals is an essential task for determining the possible infringement of a patent. This article reports a multivariate analysis method to distinguish between tablets of the same substance on the basis of their origin, by characterizing route/process specific impurities via diagnostic ion chromatograms, using liquid chromatography/mass spectrometry (LC/MS). The approach is based on the formulation of a novel index that quantifies the similarity between LC/MS samples, named the component detection weighted index of analogy. The index estimates similarity by fully exploiting the two-dimensional nature of the data, where the relative contribution of chromatograms relates to their quality and noise level. Results show that well-defined clusters are formed according to the origin of tablets; a series of ions are identified as characterizing each class and can be used to predict the origin of unknown tablet samples. The method presented is designed for analysis of larger data sets and can be suitable for exploratory analysis where any a priori knowledge on the data is scarce or absent, hence requiring the acquisition of chromatograms in a broad m/z range.

Chromatography, Liquid↗
PubMed2005

Rapid comparison of diacetylmorphine on banknotes by tandem mass spectrometry.

A procedure is described for the determination of the distribution of the contamination of banknotes with controlled drugs using tandem mass spectrometry. The method is illustrated using diacetylmorphine, which is the major active component of heroin. A series of banknotes is introduced into the mass spectrometer and the intensities of two product ions (m/z 328 and 268) derived from the precursor protonated molecule (m/z 370) are recorded. A banknote is considered contaminated if it shows a significant peak for both product ions, and if the ratio of intensities of these two peaks falls within accepted limits. The distribution of diacetylmorphine on sterling banknotes taken from general circulation within the UK can be modelled by an arcsin (square root) transformation of the data or by a log transformation of the data with a higher proportion of contamination. The two models were found to be in close agreement, predicting an upper limit (at 99.9% confidence) of contamination on banknotes from general circulation between 9 and 10%. The percentage contamination in a case study was calculated and compared to the background distribution using the two models proposed. This comparison revealed that the contamination present in the case study was inconsistent with that present on banknotes in general circulation.

Forensic Sciences↗
PubMed2004

Toxicological classification of urine samples using pattern recognition techniques and capillary electrophoresis.

In toxicology, hazardous substances detected in organisms may often lead to different pathological conditions depending on the type of exposure and level of dosage; hence, further analysis on this can suggest the best cure. Urine profiling may serve the purpose because samples typically contain hundreds of compounds representing an effective metabolic fingerprint. This paper proposes a pattern recognition procedure for determining the type of cadmium dosage, acute or chronic, administrated to laboratory rats, where urinary profiles are detected using capillary electrophoresis. The procedure is based on the composition of a sample data matrix consisting of areas of common peaks, with appropriate pre-processing aimed at reducing the lack of reproducibility and enhancing the potential contribution of low-level metabolites in discrimination. The matrix is then used for pattern recognition including principal components analysis, cluster analysis, discriminant analysis and support vector machines. Attention is particularly focussed on the last of these techniques, because of its novelty and some attractive features such as its suitability to work with datasets that are small and/or have low samples/variable ratios. The type of cadmium administration is detected as a relevant feature that contributes to the structure of the sample matrix, and samples are classified according to the class membership, with discriminant analysis and support vector machines performing complementarily on a training and on a test set.

Animals↗
PubMed2004

Genotyping using single nucleotide polymorphism, fluorescence spectroscopy and pattern recognition.

This paper describes a method for genetic screening using single nucleotide polymorphism. Fluorescence spectra with an excitation frequency of 488 nm are recorded over a range of 550 to 660 nm of fragments of human DNA together with two fluorescent probe dyes attached to specific primers, one for each type of allele and a background dye, prepared using the Taqman reaction. The fluorescence spectra are monitored and principal components analysis used to separate spectra into three groups, which are visually identified as allele 1 (wild type), allele 2 (mutant) and mixed allele by comparison to reference samples. Malahanobis distance using 4 principal components are used to correctly classify samples into groups.

DNA↗
PubMed2003

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.

Gas Chromatography-Mass Spectrometry↗
PubMed2002

Estimation of second order rate constants using chemometric methods with kinetic constraints.

Several methods are described for determining rate constants for second order reactions of the form U + V --> W using chemometrics and hard modelling to analyse UV absorption spectroscopic data, where all species absorb with comparable concentrations and extinctions. An interesting feature of this type of reaction is that the number of steps in the reaction is less than the number of absorbing species, resulting in a rank-deficient response matrix. This can cause problems when using some of the methods described in the literature. The approaches discussed in the paper depend, in part, on what knowledge is available about the system, including the spectra of the reactants and product, the initial concentrations and the exact kinetics. Sometimes some of this information may not be available or may be hard to estimate. Five groups of methods are discussed, namely use of multiple linear regression to obtain concentration profiles and fit kinetics information, rank augmentation using multiple batch runs, difference spectra based approaches, mixed spectral approaches which treat the reaction as two independent pseudospecies, and principal components regression. Two datasets are simulated, one where the spectra are quite different and the other where the spectrum of one reactant and the product share a high degree of overlap. Three sources of error are considered, namely sampling error, instrumental noise and errors in initial concentrations. The relative merits of each method are discussed.

Journal Article↗
PubMedDate not supplied

Diagnostic pattern recognition on gene-expression profile data by using one-class classification.

In this paper, we perform diagnostic pattern recognition on a gene-expression profile data set by using one-class classification. Unlike conventional multiclass classifiers, the one-class (OC) classifier is built on one class only. For optimal performance, it accepts samples coming from the class used for training and rejects all samples from other classes. We evaluate six OC classifiers: the Gaussian model, Parzen windows, support vector data description (with two types of kernels: inner product and Gaussian), nearest neighbor data description, K-means, and PCA on three gene-expression profile data sets, those being an SRBCT data set, a Colon data set, and a Leukemia data set. Providing there is a good splitting of training and test samples and feature selection, most OC classifiers can produce high quality results. Parzen windows and support vector data description are "over-strict" in most cases, while nearest neighbor data description is "over-loose". Other classifiers are intermediate between these two extremes. The main difficulty for the OC classifier is it is difficult to obtain an optimum decision threshold if there are a limited number of training samples.

Colon↗
PubMedDate not supplied

Hard modeling methods for the curve resolution of data from liquid chromatography with a diode array detector and on-flow liquid chromatography with nuclear magnetic resonance spectroscopy.

Hard modeling methods have been performed on data from high-performance liquid chromatography with a diode array detector (LC-DAD) and on-flow liquid chromatography with 1H nuclear magnetic spectroscopy (LC-NMR). Four methods have been used to optimize parameters to model concentration profiles, three of which belong to classical optimization methods (the simplex method of Nelder-Mead, sequential quadratic programming approach, and Levenberg-Marquardt method), and the fourth is the application of genetic algorithms using real-value encoding. Only classical methods worked well for LC-DAD data, while all of the methods produced good results when LC-NMR data were divided into small spectral windows of peak clusters and parameters were optimized over each window.

Journal Article↗