Raman spectroscopy is a powerful molecular fingerprinting method which measures the vibrational modes of molecules to identify and quantify chemical species. In biomedical spectroscopy, where samples are usually complex mixtures of many molecules, Raman spectra give a biochemical “portrait” that can be used to discriminate between distinct samples. One major technical challenge in implementing Raman spectrometer sensors is the technique’s low intrinsic signal to noise ratio. To amplify the Raman signal, a number of different approaches can be applied. In this thesis two techniques are used;​ surface enhanced Raman scattering (SERS) from metal nanoparticles along with light-matter interaction enhancement from co-coupling light and sample to a liquid core waveguide.

In order to process the complex spectral data arising from these sensors, a robust signal processing method is required. To this end, we have developed and validated a machine learning spectral analysis platform based on genetically optimized support vector machines (GA-SVM). This work is the subject of Chapter 3. We found that the GA-SVM significantly outperformed the standard statistical based modelling approach, partial least squared, in regression tasks for several different biomedical Raman applications. Furthermore, we found that the use of more complex kernel functions in the SVM yielded superior results. The genetic optimization algorithm was necessary to use these more complex kernel functions because its computation time scales linearly with complexity, whereas the standard brute force approach scales exponentially.

Chapter 4 concerns the development of a Raman sensor used to quantify and identify pathogenic bacteria. This device centres on a microfluidic flow cell which forces bacteria to flow through a hollow-core photonic crystal fiber (HC-PCF) to which the Raman excitation laser is also coupled. The bacteria are also mixed with silver nanoparticles to simultaneously achieve SERS and light-matter interaction enhancement in the sensor. Overall, the fiber and nanoparticles yield a bulk enhancement of 400x for the Raman spectrum. Bacteria are quantified in this system by counting the number of “spectral events” that occur as cells flow through the HC-PCF in a 15-minute window. This approach achieved very high linearity, as well as an average detection limit of 3.7 CFU/mL. In addition, bacteria are identified by using the same GA-SVM algorithm developed in the preceding chapter. These machine learning models achieved a discrimination accuracy of ~92% when comparing the spectra of the bacteria S. aureus, P. aeruginosa, and E. coli. In mixed samples of bacteria, the error of quantification increased significantly to 13.3 CFU/mL, but the output of the sensor was highly correlated with the ground-truth bacterial load.

In Chapter 5 we outline the development of a diagnostic scheme for chemoresistance in ovarian cancer based on SERS measurements from cysteine-capped gold nanoparticles. Resistance to chemotherapy was determined based on three factors:​ the concentration of tumor derived exosomes, the chemical composition of the exosomes, and the concentration of exosome-derived cisplatin. Cisplatin is the drug of interest for this problem, as it is the most basic chemotherapy agent. The system works by first incubating the gold nanoparticles with tumor derived exosomes. The cisplatin therein causes the particles to destabilize slightly, resulting in the aggregation rate of the nanoparticles being proportional to the drug concentration. At steady state aggregation, the magnitude of the Raman spectrum is proportional to the exosome concentration, and the spectrum contains its chemical identity. Using in vitro cancer cell lines, we found that resistant cells tend to produce more exosomes and excrete a higher concentration of cisplatin within them. Overall, this sensor exhibited good diagnostic power for chemoresistance particularly in the most common subtype in ovarian cancer.