Leong S.X.Leong Y.X.Tan E.X.Sim H.Y.F.Koh C.S.L.Lee Y.H.Chong C.Ng L.S.Chen J.R.T.Pang D.W.C.Nguyen L.B.T.Boong S.K.Han X.Kao Y.C.Chua Y.H.Phan-Quang G.C.Phang I.Y.Lee H.K.Abdad M.Y.Tan N.S.Ling X.Y.Mahidol University2023-06-182023-06-182022-02-22ACS Nano Vol.16 No.2 (2022) , 2629-263919360851https://repository.li.mahidol.ac.th/handle/20.500.14594/84601Population-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.EngineeringArticleSCOPUS10.1021/acsnano.1c093712-s2.0-851239291501936086X35040314