| EPSRC Reference: |
EP/V026488/2 |
| Title: |
VIPIRS - Virus Identification via Portable InfraRed Spectroscopy |
| Principal Investigator: |
Wang, Professor H |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Electronics, Elec Eng & Comp Sci |
| Organisation: |
Queen's University of Belfast |
| Scheme: |
Standard Research |
| Starts: |
01 August 2021 |
Ends: |
31 January 2022 |
Value (£): |
288,821
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| EPSRC Research Topic Classifications: |
| Instrumentation Eng. & Dev. |
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| EPSRC Industrial Sector Classifications: |
| Healthcare |
Pharmaceuticals and Biotechnology |
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| Panel History: |
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Summary on Grant Application Form |
Spectroscopic techniques such as infra-red, Raman, and mass spectrometry have long been used to identify chemical compounds and biological species, including bacteria and viruses, usually in specialised lab conditions with high performance instrumentation. Virus identification in realistic clinical/field environments, using low cost instrumentation, is appealing, as it can be widely deployed and so is very suitable for diagnosis, prevention and management in pandemics such as COVID-19. However, low cost instrumentation produces poorly-resolved spectra with added noise. Our recent work has investigated machine learning algorithms applied to spectra from low cost near infra-red (NIR) spectrometers to extract identifiable patterns from targets with complex backgrounds and limited experimental control/processing. Our latest study shows that it is possible to use the technique to accurately differentiate respiratory syncytial virus and Sendai virus in different media, and quantify their viral loads.
We aim to develop a spectrometer-fronted, cloud-based system for in-situ SARS-CoV-2 detection
with three deliveries. The system will record spectra from patient nasal samples in the field and return a positive/negative diagnosis within ~ 1 minute, based on model-driven analytics running on a cloud-based service. The detection model will be developed, trained and validated using
spectra from the SARS-CoV-2 virus in (a) lysis buffer and (b) nasal aspirate simulant; the model will then be used to determine whether the virus is present in the sample using a 'subsumption' operation in the learning algorithm. The system will be validated in real environments in
collaboration with our partners in Northern Ireland Regional Virology Lab (RVL).
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.qub.ac.uk |