A Traumatic Brain Injury (TBI) occurs when brain tissue is damaged as a result of an external mechanical force. It is a global health issue which, when serious, can result in death or permanent disability. The damage caused by the initial trauma continues to evolve in the days following the incident and therefore patients with a serious TBI are closely monitored so that, if any change in the condition of the brain is detected, an appropriate medical intervention can be made. Metabolic profiling of extracellular brain fluid can be used to monitor brain health, as it provides data on the efficiency of metabolic processes taking place within the brain. If metabolism is disrupted, causing energy failure in the brain, it can lead to the destruction of brain cells and a poor patient outcome.

D-glucose, L-lactate and pyruvate are the metabolic intermediates most commonly monitored following TBI. Their respective concentrations are currently measured via a two-stage process: the constituents of the extracellular brain fluid are first extracted by microdialysis and are then analysed via a series of colorimetric assays which are carried out using a commercially-available analyser. The use of this analyser presents a number of drawbacks which have prevented the widespread use of metabolic profiling in the management of TBI. For example, the process of manually loading samples into the analyser is both time- and labour-intensive and therefore is carried out only intermittently by clinical staff. As a result, metabolic profiling data cannot be obtained in real time and any necessary medical intervention may consequently be delayed.

This thesis describes the work undertaken to develop a spectroscopic method, and an associated statistical model, for the detection of glucose, lactate and pyruvate that could be directly integrated into the existing microdialysis setup and thus used for continuous metabolic profiling of TBI patients. The techniques of Raman spectroscopy and Fourier Transform Infra-Red (FTIR) spectroscopy were investigated to determine whether they could selectively detect glucose, lactate and pyruvate in solution and whether they possessed the sensitivity required to detect those compounds at clinically-relevant concentrations. Consideration was also given to the practicalities of adapting each technique to the specific requirements of the clinical setting in which it would be used. These initial studies indicated that FTIR spectroscopy showed the greater promise for development into a sensor and therefore subsequent work focused only on this technique. A detailed study was carried out in order to compare various FTIR spectroscopy methods. The data generated were used to build and test a statistical model for the prediction of clinically-relevant glucose, lactate and pyruvate concentrations from their FTIR spectra. The results of this work provide important insights into the capabilities of currently-available FTIR techniques, when used in combination with predictive statistical modelling, and will be used to guide the next phase of sensor development.