Raman spectroscopy is a molecularly specific technique which allows identification of analytes due to their unique vibrational fingerprint. However, normal Raman is limited in its ability to detect analytes at depth (Raman microscopy is generally limited to depths in the order of microns). Therefore, the use of spatially offset Raman spectroscopy (SORS) is an emerging technique capable of providing spectral information from the analyte, even when obscuring barriers such as skin, tissue and bone are present. SORS makes use of a spatial offset between the point of laser excitation and the point of collection which makes it possible to measure Raman scattered photons generated by analytes at depth. This is unlike conventional backscattering Raman techniques, where excitation and collection typically take place at the same point. However, Raman scattering is intrinsically weak, particularly at the higher (NIR) excitation wavelengths required to penetrate through biological samples, since Raman scattering intensity has a 4th power dependence on excitation frequency. Therefore, it is proposed to use surface enhanced Raman scattering (SERS), to enhance the Raman signal this leads to the new technique of surface enhanced spatially offset Raman (SESORS).

This project proposes to use specifically designed functionalised metal nanoparticles as optical imaging probes to target disease models e.g. bacterial biofilms and cancer at depth through skin and tissue. This will be achieved using functionalised nanoparticles which will be designed to display a unique Raman response in response to target recognition. In this project we will investigate unique molecules for SESORS as well as longer Raman excitations wavelengths. In addition, SESORS is a new approach, and we will initially use model systems e.g. tissue phantoms to optimise the nanoparticles and response to give a strong SERS response. This will allow them to be imaged at greater depths and also allow us to develop data analysis methodologies to create images in 2 and 3 dimensions. We will also expand on our recent work on correlating signal with nanoparticle depth and into how the different types of tissue, muscular, lipid rich etc, effects the signal and depth prediction.