Analysis of badger urine volatiles using gas chromatography-mass spectrometry and pattern recognition techniques.

The potential for badger urine to signal olfactory information relating to sex, age class and seasonality was investigated by performing GCMS headspace analysis followed by pattern recognition statistical analysis on 84 urine samples collected from different categories of animal. Approximately 300 compounds were identified using library searching, and GCMS peak areas were recorded for the 33 most common. PCA was performed on the normalised and standardised data from all badgers, through which significant seasonal trends and groupings of homologous series of compounds were detected. PCA was also performed on the three subgroups of adults in the spring, summer and autumn, and a level of sexual discrimination was possible during the latter two seasons. Malanobis distances on the scores of the first five principal components provided good discrimination for these three subgroups, but discrimination was poor when all samples were analysed together. This, combined with the initial results of the PCA, confirms that a strong seasonal trend is imposed upon the sexual trend in this dataset. Our initial analysis indicates that badger urine potentially contains olfactory cues relating to sex and season. The relevance of these findings to understanding olfactory communication in mammals is discussed.

Principal components scores and loadings plots for visualisation of the electrospray ionisation liquid chromatography mass spectra of a mixture of chlorophyll degradation products at different cone voltages.

Positive ion electrospray liquid chromatography mass spectrometry was performed on a mixture of allomers obtained from the degradation of chlorophyll a, at three cone voltages. An approach is described for obtaining principal components loadings and scores plots, involving mass selection, normalisation and standardisation of the data, principal components analysis and three dimensional projections. The loadings plots group ions which are assigned to five major compounds in the mixture by reference to the scores. At higher cone voltage fragmentation and differentiation between compounds with identical molecular weights is observed.

Chemometric analysis of high performance liquid chromatography-diode array detection-electrospray mass spectrometry of 2- and 3-hydroxypyridine.

Triply coupled high performance liquid chromatography using diode array detection and positive ion electrospray mass spectrometry of 2- and 3-hydroxypyridine is presented. Considerations of the physical method for coupling the two detectors, the influence of pH on retention times, the cone voltage of the mass spectrometer and the linear concentration ranges are described. Data from both detectors are aligned and interpolated. The analyte mass spectra are reduced to 20 significant masses. Principal components plots on the raw, normalised and standardised data, derivatives to determine composition 1 regions, deconvolution and procrustes analysis to compare data from both detectors are discussed. Common trends in both mass spectral and diode array chromatograms are interpreted. This paper represents a new approach to common processing of chromatographic data from two detectors.

Monitoring of impurities using high-performance liquid chromatography with diode-array detection: eigenvalue plots of partially overlapping tailing peaks.

The chromatogram of ropinirole in the presence of about 5% of a closely eluting impurity, obtained by HPLC with diode-array detection, was analysed by chemometric procedures. Log eigenvalue plots were used to determine the relative composition of regions of the chromatogram. It is shown that since the peaks exhibit tailing, unusual behaviour is found in the plots. This is verified by performing simulations, in which it is demonstrated that peak asymmetry has a pronounced influence on this chemometric approach. In many cases of liquid chromatographic analysis, asymmetric peak shapes are encountered and methods for peak purity assessment should be re-evaluated in the light of these asymmetries.

Cross-validatory selection of test and validation sets in multivariate calibration and neural networks as applied to spectroscopy.

Cross-validated and non-cross-validated regression models using principal component regression (PCR), partial least squares (PLS) and artificial neural networks (ANN) have been used to relate the concentrations of polycyclic aromatic hydrocarbon pollutants to the electronic absorption spectra of coal tar pitch volatiles. The different trends in the cross-validated and non-cross-validated results are discussed as well as a method for the production of a true cross-validated neural network regression model. It is shown that the methods must be compared through the errors produced in the validation sets as well as those given for the final model. Various methods for calculation of errors are described and compared. The separation of training, validation and test sets into fully independent groups is emphasized. PLS outperforms PCR using all indicators. ANNs are inferior to multivariate techniques for individual compounds but are reasonably effective in predicting the sum of PAHs in the mixture set.

Detection of quinine and its metabolites in horse urine by gas chromatography-mass spectrometry.

After oral administration of quinine sulfate to a thoroughbred mare, seven urine samples were obtained over a 45.5 h period. Using gas chromatography -electron impact ionization and positive-ion chemical ionization mass spectrometry, quinine and five putative metabolites were detected and tentatively identified in enzyme-hydrolysed post-administration urine; all metabolites involved some form of oxidation. The parent drug could be detected for about 16 h and some phase I biotransformation products for up to 40 h post-administration.

Chemometric studies of the effect of toxic metals on plants: the use of response surface methodology to investigate the influence of Tl, Cd and Ag on the growth of cabbage seedlings.

The growth of cabbage seedlings at five concentrations of Cd, Tl and Ag was monitored by measuring their wet weights, root and shoot lengths after four week's growth in a controlled environment. Toxicity curves were constructed using a three factor central composite experimental design. The interaction and competition between any two metals were studied by displaying response surfaces. Ag was observed to be the most toxic, while Tl and Cd, although toxic, exhibited fairly similar effects. Analysis of variance was used to test for the significance of each of the computed parameters.