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In this work lung cancer is introduced along with the current detection methods. The inadequacies of the current situation are highlighted along with the need for better detection technologies that would allow for a more rigorous testing regime to be implemented. Metabolism and metabolites are introduced as potential biomarkers. The advanced detection techniques mass spectrometry (MS) and differential mobility spectrometry (DMS) are introduced and discussed with regard to being a fieldable device. The methods applicable to processing data generated by these instruments are discussed. Finally the research objectives are highlighted. The science of breath sampling is discussed along with the considerations when engaging in breath analysis research. Sampling and trapping of volatile organic compounds (VOCs) is discussed with particular emphasis on the adaptive breath sampler which was used in this work. The benefits of a dual detector instrument allowing for analysis of a single sample using both MS and DMS are outlined. The design and implementation of a parallel, two detector system is outlined including the intricacies of balancing the two columns that operate at different pressures and developing a mount Processing DMS data currently lags behind the current hardware available as there are no methods that allow the full data surface to be utilised. This work outlines a method for transforming DMS data from three dimensions to two dimensions while retaining the full information contained within the data surface. This method was tested with generated data sets to show its' utility and compared to the current standard processing method using real data sets. An understanding of all aspects of a clinical research project is vital to ensure the smooth running and completion of the project. The currently required documentation for an outside researcher to work within the NHS are detailed along with the expected timeframe for each step of designing, gaining ethical approval and implementing the research. The use of Gantt charts and work flow diagrams is highlighted and examples are given. An initial inspection of the data produced by a pilot study shows that there a several challenges that must be overcome, these are contamination and artefact peaks, retention time shifting, unresolved peaks, differing intensities in similar samples and the complexities of correctly identifying compounds found in breath samples. These are discussed and a workflow is highlighted.

Diabetes mellitus is a growing health problem worldwide. Suitable long-term control and management of this disease are enabled by determination of glycated haemoglobin (HbA1c) in blood. The results are given as %HbA1c of total haemoglobin. Presently available tests vary in cost and convenience and there is an identified need to introduce improved equipment for self-monitoring. This dissertation focuses on fast and straightforward detection of glycated haemoglobin (HbA1c) using cyclic voltammetry and chronoamperometry. Haemoglobin was determined by monitoring its reaction with potassium ferricyanide on screen printed electrodes at an oxidative potential +500 mV. A working electrode was modified with carbon nanotubes to enhance electron transfer. A calibration curve was linear in a range from 0.83 to 83 mg/mL. Another innovative approach to detecting haemoglobin using its enzymatic activity was also developed. Detection of haemoglobin was performed with hydroquinone and hydrogen peroxide on screen printed electrodes at a potential -400 mV in a Flow Injection Analysis system (FIA). Cont/d.

Ventilator associated pneumonia (VAP) is a significant challenge for the Intensive Care doctors worldwide. It is both difficult to diagnose accurately and quickly and to treat effectively once the diagnosis has been established. Current diagnostic microbiological methods of diagnosis can take up to 48 hours to yield results. Early diagnosis and treatment remain the best way of improving outcome for patients with VAP. In this study we look at novel diagnostic techniques for VAP. Electronic nose (Enose) technology was used to identify to identify the presence of microorganisms in bronchoalveolar lavage (BAL) fluid samples taken from the respiratory tracts of ventilated patients. The results were compared with standard microbiological culture and sensitivities. The Enose was able to discriminate 83% of samples into growth or no growth groups on samples grown in the lab. When the technique was employed to samples taken directly from patients the accuracy fell to 68.2%. This suggests that patient related factors may be reducing the accuracy of the Enose classification. The use of antimicrobial drugs prior to patient sampling is likely to have played a major role. The second part of this study used Gas Chromatography-Mass Spectrometry (GC-MS) analysis of patient’s breath in an attempt to identify patients with VAP. Breath samples were taken at the same time as the bronchoalveolar lavage samples described above. The use of this technique did show differences between the breath samples of patients who did not have any microbiological growth from their BAL samples and those that did. Leave one out cross validation of a PC fed LDA model showed 84% correct classification between healthy volunteers, no growth and growth groups. Finally, we evaluated the Breathotron, which is a breath analysis device designed and built at Cranfield Health. It allows for analysis of breath samples using a single sensor system as opposed to a sensor array employed in traditional Enose devices. This allows it to be more portable and cheaper to build. The Breathotron also allows collection of breath onto sorbent cartridges for GC-MS analysis. Its single sensor did not allow for accurate discrimination between samples.