Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-Infrared Spectroscopy and Chemometrics
Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-Infrared Spectroscopy and Chemometrics
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DOI：
10.1109/jsen.2022.3207222
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发表时间：
2023-05-01
影响因子：
4.3
通讯作者：
Maguire，Paul
中科院分区：
文献类型：
--
作者：
Song，Weiran;Wang，Hui;Maguire，Paul
有证据表明，有可能使用拉曼和红外(IR)光谱等技术来光学检测病毒和病毒感染，从而为快速识别感染患者提供了可能性。然而，高分辨率拉曼和红外光谱仪器是以实验室为基础的，需要熟练的操作员。使用采用类似光学方法的低成本便携式或现场可部署仪器将是非常有利的。在这项工作中，我们将化学计量学应用于低分辨率近红外(NIR)反射/吸收光谱，以探索适合广泛社会部署的简单、低成本病毒检测的可能性。我们提出了结合近红外光谱(NIRS)和化学计量学来区分两种呼吸道病毒，呼吸道合胞病毒(RSV)和仙台病毒(SEV)，呼吸道合胞病毒(RSV)是全球婴儿严重下呼吸道感染的主要原因，仙台病毒(SEV)是典型的副粘病毒。使用低成本的便携式光谱仪，在长期和短期实验中收集了分散在磷酸盐缓冲盐水(PBS)和Dulbecco的改良Eagle介质(DMEM)中的三组RSV和SeV光谱。用偏最小二乘判别分析方法对光谱数据进行预处理，并进行病毒类型和浓度分类。此外，通过数据投影在低维空间中可视化了病毒类型/浓度的可分离性。PBS和DMEM的病毒类型分类准确率最高，分别为85.8%和99.7%。结果表明，使用便携式近红外光谱作为快速、现场和低成本的RSV和SeV病毒预筛选的有价值的工具是可行的，并进一步有可能将其扩展到其他呼吸道病毒，如SARS-CoV-2。
There is evidence that it may be possible to detect viruses and viral infection optically using techniques such as Raman and infrared (IR) spectroscopy and hence open the possibility of rapid identification of infected patients. However, high-resolution Raman and IR spectroscopy instruments are laboratory-based and require skilled operators. The use of low-cost portable or field-deployable instruments employing similar optical approaches would be highly advantageous. In this work, we use chemometrics applied to low-resolution near-IR (NIR) reflectance/absorbance spectra to investigate the potential for simple low-cost virus detection suitable for widespread societal deployment. We present the combination of near-IR spectroscopy (NIRS) and chemometrics to distinguish two respiratory viruses, respiratory syncytial virus (RSV), the principal cause of severe lower respiratory tract infections in infants worldwide, and Sendai virus (SeV), a prototypic paramyxovirus. Using a low-cost and portable spectrometer, three sets of RSV and SeV spectra, dispersed in phosphate-buffered saline (PBS) medium or Dulbecco’s modified eagle medium (DMEM), were collected in long- and short-term experiments. The spectra were preprocessed and analyzed by partial least-squares discriminant analysis (PLS-DA) for virus type and concentration classification. Moreover, the virus type/concentration separability was visualized in a low-dimensional space through data projection. The highest virus-type classification accuracy obtained in PBS and DMEM is 85.8% and 99.7%, respectively. The results demonstrate the feasibility of using portable NIR spectroscopy as a valuable tool for rapid, on- site, and low-cost virus prescreening for RSV and SeV with the further possibility of extending this to other respiratory viruses such as SARS-CoV-2.