Broadband Spectral Interferometric Polarized Coherent anti-Stokes Raman Scattering - a non-linear approach to fast all-optical chemical fingerprinting
Lead Research Organisation:
King's College London
Department Name: Physics
Abstract
Development in coherent anti-Stokes Raman spectroscopy (CARS) techniques has expanded greatly in the past two decades with research geared towards faster and more sensitive data acquisition, simplified experimental requirements and robust computational/optical methods to remove the associated non-resonant background (NRB) with CARS. Following on from research conducted by David Richards' group at KCL, this project aims to further expand the capabilities of the broadband CARS technique developed and patented by the group, spectral interferometric polarized CARS (SIPCARS), as well as work on broadband CARS in general. There are two main strands as to which this will be achieved; experimental studies and data analysis of hyperspectral CARS data.
The experimental strand will encompass both developments to the SIPCARS system and the usage of complex biological samples to characterise and demonstrate the validity and usefulness of SIPCARS as a means for obtaining rapid and accurate spectral data of biological specimen.
The data analysis strand will primarily focus on analysing hyperspectral data obtained from SIPCARS measurements. Typically, analysis of hyperspectral data is carried out using clustering techniques. Essentially, the purpose of this strand in the research programme will be to provide tools to extract information from biological hyperspectral data, thus creating a fully comprehensive system capable of measuring and analysing hyperspectral Raman data of complex biological samples with rapid acquisition rates and a high level of accuracy.
The experimental strand will encompass both developments to the SIPCARS system and the usage of complex biological samples to characterise and demonstrate the validity and usefulness of SIPCARS as a means for obtaining rapid and accurate spectral data of biological specimen.
The data analysis strand will primarily focus on analysing hyperspectral data obtained from SIPCARS measurements. Typically, analysis of hyperspectral data is carried out using clustering techniques. Essentially, the purpose of this strand in the research programme will be to provide tools to extract information from biological hyperspectral data, thus creating a fully comprehensive system capable of measuring and analysing hyperspectral Raman data of complex biological samples with rapid acquisition rates and a high level of accuracy.
Organisations
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/N509498/1 | 01/10/2016 | 30/09/2021 | |||
2188482 | Studentship | EP/N509498/1 | 01/07/2016 | 15/12/2020 | Priyank Shah |
Description | Extension and a more complete understanding of a novel technique to capable of acquiring hyperspectral (images where each pixel contains a spectrum which serves as a unique fingerprint determining what molecules are present) images of chemical/biological data and, in particular, the specific advantages and disadvantages pertaining to the technique. |
Exploitation Route | The novel technique used is part of a broader set of research into techniques capable of rapidly acquiring hyperspectral data. Fields that this lends itself to include healthcare (particularly histopathology), bio/chemical physics and pharmaceuticals. |
Sectors | Chemicals,Healthcare,Pharmaceuticals and Medical Biotechnology |
Title | ScanCARS |
Description | Software to control hardware for acquiring spectral/hyperspectral data using specific scientific cameras, spectrometers and data acquisition devices. |
Type Of Technology | Software |
Year Produced | 2017 |
Open Source License? | Yes |
Impact | Simplification of designing and maintaining a measurement and control sottware (alternatives being e.g. Labview based on a graphical programming paradigm). In particular, the software is written in python making it substantially more maintainable. |
URL | https://github.com/priyankshah7/ScanCARS |