A Breathalyzer for Detecting COVID and Other Diseases
NIST/JILA fellows Jun Ye, David Nesbitt and their colleagues have demonstrated that a breathalyzer based on Nobel Prize-winning frequency-comb technology combined with machine learning techniques can accurately detect SARS-CoV-2 infection in human breath. The frequency-comb technology, recognized in the 2005 Nobel Prize in physics, has the potential to detect many other diseases, such as COPD, lung cancer, and kidney failure. Frequency combs can precisely measure different colors of light, including the infrared light absorbed by molecules in a person’s breath. Combined with machine learning, this technique can detect the presence of specific combinations of molecules that are signatures of disease.
JILA researchers have upgraded a breathalyzer based on Nobel Prize-winning frequency-comb technology and combined it with machine learning to detect SARS-CoV-2 infection in 170 volunteer subjects with excellent accuracy. Their achievement represents the first real-world test of the technology’s capability to diagnose disease in exhaled human breath.
Frequency comb technology has the potential to non-invasively diagnose more health conditions than other breath analysis techniques while also being faster and potentially more accurate than some other medical tests. Frequency combs act as rulers for precisely measuring different colors of light, including the infrared light absorbed by biomolecules in a person’s breath.
NIST/JILA’s Frequency Comb Breathalyzer
NIST/JILA Fellows Jun Ye and David Nesbitt built a breathalyzer that identifies biomarkers of disease by measuring the colors and amounts of light absorbed as a laser frequency comb passes through breath samples inside a glass tube.
Human breath contains more than 1,000 different trace molecules, many of which are correlated with specific health conditions. JILA’s frequency comb breathalyzer identifies chemical s ..
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