Biomedical subjects

Simeone Zomer

Publications and source records attributed to Simeone Zomer.

4 recordsLinked to original sources

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

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↗

PubMed2004

Multivariate analysis and classification of two-dimensional angular optical scattering patterns from aggregates.

Two-dimensional light-scattering patterns from aggregates have undergone feature extraction followed by multivariate statistical analysis. The aggregates are comprised of primary particles of varying shape and size. Morphological descriptors (features) were extracted by a nonlinear filtering algorithm (spectrum enhancement) and then processed by principal component analysis and discriminant function analysis. The analysis was performed on two data sets, one in which the aggregates had a fixed primary particle size but varied in overall dimension and another in which the aggregate size was fixed but the primary particle size varied. Classification of the samples was performed adequately, providing some distinction among the limited classes that were analyzed.

Journal Article↗

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↗