An efficient primary screening of COVID‐19 by serum Raman spectroscopy

• Published in: Journal of Raman spectroscopy Vol. 52; no. 5; pp. 949 - 958
• Main Authors: Yin, Gang, Li, Lintao, Lu, Shun, Yin, Yu, Su, Yuanzhang, Zeng, Yilan, Luo, Mei, Ma, Maohua, Zhou, Hongyan, Orlandini, Lucia, Yao, Dezhong, Liu, Gang, Lang, Jinyi
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01-05-2021; John Wiley and Sons Inc
• Subjects: Accuracy; Algorithms; Classification; Confidence intervals; Coronaviruses; COVID-19; Datasets; Learning algorithms; Machine learning; Pandemics; Polymerase chain reaction; Raman spectroscopy; screening, support vector machine; Spectroscopy; Spectrum analysis; Statistical analysis; Statistical methods; Viral diseases; COVID‐19; screening, support vector machine; Raman spectroscopy; machine learning
• ISSN: 0377-0486; 1097-4555
• DOI: 10.1002/jrs.6080

The outbreak of COVID‐19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID‐19 detection is the real‐time polymerase chain reaction (RT‐PCR)‐based technique; however, it also has certain limitations, such as sample‐dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID‐19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID‐19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support‐vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID‐19 patients and 5 symptomatic COVID‐19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups—confirmed COVID‐19, suspected, and healthy individuals—the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID‐19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85–0.88), and the accuracy between the COVID‐19 and the healthy controls is 0.90 (95% CI: 0.89–0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67–0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum‐level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID‐19 screening.
The outbreak of COVID‐19 coronavirus disease around the end of 2019 has become a pandemic. We propose a safe and efficient method for screening COVID‐19 based on Raman spectroscopy. Raman spectroscopy is adopted to analyze these sealed serum samples, and a machine learning support‐vector machines (SVMs) method is applied to the spectrum dataset to build a diagnostic algorithm. Our results suggest that Raman spectroscopy could be safe and efficient technique for COVID‐19 screening.