Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach.
Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach.
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DOI：
10.3390/s24010308
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发表时间：
2024-01-04
期刊：
影响因子：
--
通讯作者：
Maguire P
中科院分区：
文献类型：
--
作者：
Huang JD;Wang H;Power U;McLaughlin JA;Nugent C;Rahman E;Barabas J;Maguire P
呼吸道病毒的检测对于应对COVID-19等大流行病至关重要。传统方法通常需要实验室的高成本设备。一种新兴的替代方法是近红外（NIR）光谱法，特别是便携式光谱法，其具有低成本、便携性、快速性、易用性以及在临床和现场环境中的大规模部署能力的优点。有效应用的一个障碍在于其共同的局限性，包括相对较低的特异性和一般质量。特征性地，光谱曲线显示病毒存在和病毒不存在的样品的交织特征。这引发了使用机器学习方法来克服困难的想法。虽然随后的障碍与直接部署机器学习方法导致建模结果准确性不足的事实相吻合。本文介绍了一种数据驱动的研究，使用便携式近红外光谱仪检测两种常见的呼吸道病毒，呼吸道合胞病毒（RSV）和仙台病毒（SEV），该光谱仪由机器学习解决方案支持，该解决方案通过变量投影重要性（VIP）得分及其分位数值的变量选择算法增强，沿着变量截断处理，在一定程度上克服障碍。我们在专门开发的变量选择算法的帮助下进行了广泛的实验，总共使用了四个数据集，实现了分类精度：（1）RSV、SEV和RSV + SEV分别为0.88、0.94和0.93，多次运行的平均值，对于神经网络建模，依次取3个会话的数据用于训练，剩余的一个会话的“未知”数据集用于测试。(2)模型验证的平均准确度为0.94（RSV）、0.97（SEV）和0.97（RSV + SEV），模型测试的平均准确度为0.90（RSV）、0.93（SEV）和0.91（RSV + SEV），使用两个数据集进行模型训练，一个用于模型验证，另一个用于模型测试。这些结果表明，使用便携式近红外光谱技术结合机器学习来准确检测呼吸道病毒是可行的，该方法可能是人群筛查的可行解决方案。
Respiratory viruses’ detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an ‘unknown’ dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.
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影响因子：
48
作者：
Bzdok D;Krzywinski M;Altman N
通讯作者：
Altman N
影响因子：
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作者：
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