Development, validation, and application of a selective saliva-based cortisol biosensor

Cortisol, a steroid hormone, is essential in various physiological processes and follows a circadian rhythm throughout a day-night cycle. Abnormal levels of cortisol can be attributed to diseases, but most notably, it is caused by stress. Therefore, cortisol is widely known as a stress biomarker. Having a device to analyze salivary cortisol is essential in identifying environmental or behavioural triggers that can lead to stress. Analysis through saliva-based cortisol sensing offers a method of obtaining samples in a non-invasive form, minimizing additional stress. The current standard methods of measurements have disadvantages of being time-consuming, expensive and require specialized equipment to run the analysis, such as enzyme-linked immunosorbent assay (ELISA). Therefore, there is a need for a device that is cost-effective, fast, portable, and easy to use, thus creating a point-of-care (POC) device for the measurement of salivary cortisol. In this thesis, a lateral flow assay (LFA) technique have been implemented for rapid quantitative analyses of salivatory cortisol. Parametric studies performed to investigate the effects of LFA preparation and construction on the measurement of cortisol sample concentrations. The studies conducted determined the characteristics of the LFA, such as sensitivity and selectivity to establish limitations and reproducibility. Horse saliva was used to test the LFA performance and was compared to an ELISA measurement. MATLAB was used in the development of an automated image processing algorithm. This algorithm enabled automated and faster measurement of cortisol in saliva through extraction of novel transient features. It was concluded that using automated image processing and advanced feature extraction a higher level of selectivity can be achieved