VIPIRS - Virus Identification via Portable InfraRed Spectroscopy
Lead Research Organisation:
University of Ulster
Department Name: Sch of Computing & Mathematical Sci
Abstract
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).
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).
Publications
Song W
(2023)
Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-Infrared Spectroscopy and Chemometrics
in IEEE Sensors Journal
| Description | We have collected a total of 5 sessions of near infrared (NIR) spectra from respiratory syncytial virus (RSV) and Sendai virus (SEV). We raised two general research questions: (1) Can we differentiate RSV and SEV based on NIR spectra? (2) Can we detect RSV (or SEV) against the background? To answer these general questions, we have designed numerous experiments to answer specific research questions, using various protocols and data analysis methods. Initial experimental results were disappointing -- we were not able to outperform a simple baseline method -- majority voting. This remained stubbornly so for a long period of time. It was then decided to try a different approach. A new data analysis method was designed and tested it on the same data. After numerous trials and errors, good results emerged recently (February/March 2021). For the task of inter-session RSV/SEV differentiation, an average accuracy of 85% was achieved which is significantly higher than the baseline accuracy of 64%. For the task of within-session RSV/SEV differentiation, an average accuracy of 90+% was achieved. With this set of results, the first objective has been achieved with the development of a successful spectral model for respiratory virus identification in media with inter-session differentiation of respiratory syncytial virus (RSV) from Sendai virus (SEV) achieving 85% accuracy. We are confident the remaining objectives can be achieved in due course. |
| Exploitation Route | This ongoing project represents a first proof of principle effort to detect respiratory viruses using portable NIR spectroscopy. If successful it will provide impetus for development and deployment of a virus detection system for field use with instant detection. |
| Sectors | Healthcare |
| Description | Multimodal Video Search by Examples (MVSE) |
| Amount | £720,502 (GBP) |
| Funding ID | EP/V002740/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2021 |
| End | 03/2024 |
| Title | Respiratory virus (RSV/SeV/SARS-CoV-2) NIR spectra |
| Description | 14 sets of near infrared (NIR) spectra data from 14 data measurement sessions using Respiratory Syncytial Virus (RSV), Sendai Virus (SeV), and SARS-CoV-2. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Led to a data measurement protocol and analysis pipeline to differentiate different types of virus. |