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House mice (Mus domesticus) communicate using scent-marks, and the chemical and microbial composition of these 'extended phenotypes' are both influenced by genetics. This study examined how the genes of the major histocompatibility complex (MHC) and background genes influence the volatile compounds (analysed with Gas Chromatography Mass Spectrometry or GC/MS) and microbial communities (analysed using Denaturating Gradient Gel Electrophoresis or DGGE) in scent-marks produced by congenic strains of mice. The use of Consensus Principal Components Analysis is described and shows relationships between the two types of fingerprints (GC/MS and DGGE profiles). Classification methods including Support Vector Machines and Discriminant Partial Least Squares suggest that mice can be classified according to both background strain and MHC-haplotype. As expected, the differences among the mice were much greater between strains that vary at both MHC and background loci than the congenics, which differ only at the MHC. These results indicate that the volatiles in scent-marks provide information about genetic similarity of the mice, and support the idea that the production of these genetically determined volatiles is influenced by commensal microflora. This paper describes the application of consensus methods to relate two blocks of analytical data.

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.

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.

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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.

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