VIPIRS - Virus Identification via Portable InfraRed Spectroscopy

红外、拉曼和质谱等光谱技术长期以来一直用于识别化合物和生物物种，包括细菌和病毒，通常在专门的实验室条件下使用高性能仪器。在现实的临床/现场环境中使用低成本仪器进行病毒鉴定具有吸引力，因为它可以广泛部署，因此非常适合于COVID-19等大流行病的诊断、预防和管理。然而，低成本的仪器产生较差的分辨率光谱，并增加了噪声。我们最近的工作研究了应用于低成本近红外（NIR）光谱仪光谱的机器学习算法，以从具有复杂背景和有限实验控制/处理的目标中提取可识别模式。我们的最新研究表明，利用该技术可以准确区分不同培养基中的呼吸道合胞病毒和仙台病毒，并量化它们的病毒载量。我们的目标是开发一个基于光谱仪的云系统，用于现场检测SARS-CoV-2，共交付三次。该系统将在现场记录患者鼻腔样本的光谱，并根据运行在基于云的服务上的模型驱动分析，在1分钟内返回阳性/阴性诊断。检测模型将使用SARS-CoV-2病毒在(a)裂解缓冲液和(b)鼻腔吸入模拟物中的光谱进行开发、训练和验证；然后，该模型将使用学习算法中的“包容”操作来确定样本中是否存在病毒。该系统将与我们在北爱尔兰地区病毒学实验室（RVL）的合作伙伴合作，在真实环境中进行验证。

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 detectionwith 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 usingspectra 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 incollaboration with our partners in Northern Ireland Regional Virology Lab (RVL).