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A Breathalyzer for Detecting COVID and Other Diseases
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.
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NIST/JILA’s Frequency Comb Breathalyzer
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 signatures of molecules based on exact colors and amounts of infrared light absorbed by a sample of exhaled breath.
Back in 2008, Jun Ye and colleagues at JILA demonstrated the world’s first frequency comb breathalyzer, which measured the absorption of light in the near-infrared part of the optical spectrum. In 2021 they achieved a thousandfold improvement in detection sensitivity by extending the technique to the mid-infrared spectral region, where molecules absorb light much more strongly. This enables some breath molecules to be identified at the parts-per-trillion level where those with the lowest concentrations tend to be present.
The added benefit to this study was the use of machine learning. Machine learning — a form of artificial intelligence (AI) — processes and analyzes a massive, complex mélange of data from all the breath samples as measured by 14,836 comb “teeth,” each representing a different color or frequency to create a predictive model to diagnose disease.
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Machine Learning Helps Detect COVID-19 and Other Diseases in Human Breath
“Molecules increase or decrease in their concentrations when associated with specific health conditions. Machine learning analyzes this information, identifies patterns and develops reliable criteria we can use to predict a diagnosis,” said Qizhong Liang, a gradua
