Selected ion flow tube mass spectroscopy (SIFT-MS) is an analytical method for the investigation of volatile organic compounds (VOCs). It produces mass to charge (m/z) ratio ion counts with a range of 10-200 m/z. Current data analysis involves sifting through the spectra files one at a time looking for peaks of interest. This is time consuming and requires expert knowledge. This thesis proposes, implements and demonstrates a novel approach to the analysis of SIFT-MS data using multivariate techniques similar to those employed to analyse electronic nose and gas chromatography mass spectroscopy (GCMS) data. The methodology was developed using a set of samples created in the laboratory that belonged to two groups which contained different VOCs found in biological samples. The methodology requires the removal of the m/z peaks associated with the precursors, then principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) methods were evaluated for biomarker discovery and sample classification. Both methods produced excellent results, identifying the volatiles in the mixtures and being able to classify samples with 100% accuracy. This methodology was then tested using a variety of samples. Ammonia was found as a possible marker for bovine TB (Mycobacterium bovis) infection using serum samples taken from wild badgers. Discrimination results of an accuracy of 67%±6% were acquired. The number of sample needed to build the best performing model from this dataset was empirically shown to be 120. It was shown to be effective for the discrimination of serum samples from cattle taken before and after introduction of bovine TB (Mycobacterium bovis) bacteria in a clinical trial (accuracy of 85% achieved). A similar dataset pertaining to infection by Mannheimia haemolytica failed to produce models that performed as well as the others - this is suspect to be due to a poor experimental design. Finally, discrimination accuracies of 88% for urine samples collected from cattle from herds infected with Mycobacterium paratuberculosis and 90% for urine samples collected in the same bovine TB trial as above were achieved. The novel multivariate approach to SIFT-MS data analysis has been shown to be effective with a number of datasets but it is sensitive to the experimental design. Recommendation for the consideration required for analysis using this method have been made
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