Noninvasive and point-of-care surface-enhanced Raman scattering (SERS)-based breathalyzer for mass screening of coronavirus disease 2019 (COVID-19) under 5 min

• Main Authors: Leong, Shi Xuan, Leong, Yong Xiang, Tan, Emily Xi, Sim, Howard Yi Fan, Koh, Charlynn Sher Lin, Lee, Yih Hong, Chong, Carice, Ng, Li Shiuan, Chen, Jaslyn Ru Ting, Pang, Desmond Wei Cheng, Nguyen, Lam Bang Thanh, Boong, Siew Kheng, Han, Xuemei, Kao, Ya-Chuan, Chua, Yi Heng, Phan-Quang, Gia Chuong, Phang, In Yee, Lee, Hiang Kwee, Abdad, Mohammad Yazid, Tan, Nguan Soon, Ling, Xing Yi
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Medicine; Surface-Enhanced Raman Scattering; Coronavirus Disease 2019
• Online Access: https://hdl.handle.net/10356/161548
• Institution: Nanyang Technological University

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.