2019年冠状病毒病(COVID-19)
冠状病毒
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
生物信息学
拉曼散射
人口
注意事项
协议(科学)
计算机科学
拉曼光谱
纳米技术
材料科学
医学
病理
疾病
生物
物理
光学
传染病(医学专业)
替代医学
生物化学
环境卫生
基因
作者
Shi Xuan Leong,Yong Xiang Leong,Emily Xi Tan,Howard Yi Fan Sim,Charlynn Sher Lin Koh,Yih Hong Lee,Carice Chong,Li Shiuan Ng,Jaslyn Ru Ting Chen,Desmond Wei Cheng Pang,Lam Bang Thanh Nguyen,Siew Kheng Boong,Xuemei Han,Ya‐Chuan Kao,Yi Heng Chua,Gia Chuong Phan‐Quang,In Yee Phang,Hiang Kwee Lee,Mohammad Yazid Abdad,Nguan Soon Tan,Xing Yi Ling
出处
期刊:ACS Nano
[American Chemical Society]
日期:2022-01-18
卷期号:16 (2): 2629-2639
被引量:115
标识
DOI:10.1021/acsnano.1c09371
摘要
Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile organic compounds presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible analysis protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple molecular receptors to establish a robust partial least-squares discriminant analysis model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through experiment and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.
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