Frequency comb breathalyser detects COVID-19

Researchers at JILA, operated by the US National Institute of Standards and Technology (NIST) and the University of Colorado Boulder (CU Boulder), have upgraded a breathalyser based on Nobel Prize-winning frequency-comb technology and combined it with machine learning to detect SARS-CoV-2 infection with impressive accuracy.

Described in the Journal of Breath Research, their achievement is believed to represent the first real-world test of the technology’s capability to diagnose disease in exhaled human breath.

Human breath contains more than 1000 different trace molecules, many of which are correlated with specific health conditions. The most prevalent analytical technique in breath research currently is gas chromatography combined with mass spectrometry, which can detect hundreds of exhaled molecules but works slowly, typically requiring tens of minutes. Its use of chemical process also unavoidably alters breath components and presents analytical challenges to identify breath profiles accurately.

Frequency comb technology has the potential to non-invasively diagnose more health conditions than other breath analysis techniques, with frequency combs acting as rulers for precisely measuring different colours of light — including the infrared light absorbed by molecules. By measuring breath molecules in a non-destructive and real-time manner, the technology can promote a more accurate and repeatable determination of exhaled breath contents.

Back in 2008, Jun Ye and colleagues at JILA demonstrated the world’s first frequency comb breathalyser, which measured the absorption of light in the near-infrared part of the optical spectrum. In 2021 they achieved a 1000-fold 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.

“Molecules increase or decrease in their concentrations when associated with specific health conditions,” said Qizhong Liang, lead author of the new paper presenting the team’s findings. “Machine learning analyses this information, identifies patterns and develops reliable criteria we can use to predict a diagnosis.”

The JILA comb breathalyser method, tested on 170 CU Boulder students and staff from May 2021 to January 2022, was found to demonstrate excellent accuracy for detecting COVID by using machine learning algorithms on absorption patterns to predict SARS-CoV-2 infection. H₂O (water), HDO (semi-heavy water), H₂CO (formaldehyde), NH₃ (ammonia), CH₃OH (methanol) and NO₂ (nitrogen dioxide) were identified as discriminating molecules for detection of SARS-CoV-2 infection.

The team measured the accuracy of their results by creating a data graph comparing their predictions of COVID-19 against PCR test results (which have high but not perfect accuracy). On the graph, they computed a quantity known as the ‘area under the curve’ (AUC). An AUC of 1, for example, would be expected for perfectly discriminating between ambient air and exhaled breath. An AUC of 0.5 would be expected for making random guesses on whether the individuals were born on odd or even months. The researchers measured an AUC of 0.849 for their COVID-19 predictions; an AUC of 0.8 or greater for medical diagnostic data is considered ‘excellent’ accuracy.

“I do think that this comb technique is superior to anything out there,” Ye said. “The basic point is not just the detection sensitivity, but the fact that we can generate a far greater amount of data, or breath markers, really establishing a whole new field of ‘comb breathomics’ with the help of AI. With a database, we can then use it to search and study many other physiological conditions for human beings and to help advance the future of health care.”

In the future, the researchers could further increase the accuracy by expanding the spectral coverage, analysing the patterns with more powerful AI techniques, and measuring and analysing additional molecules, which could include the SARS-CoV-2 virus itself. Researchers would need to build a database of the specific IR colours absorbed by the virus (its spectral ‘fingerprint’) to potentially measure viral concentrations in the breath.

The researchers also identified significant differences in breath samples based on tobacco use and a variety of gastrointestinal symptoms such as lactose intolerance, suggesting broader capability of the technique for diagnosing different sets of diseases. Indeed, the researchers are planning further studies to try to diagnose other conditions such as chronic obstructive pulmonary disease, the third leading cause of death worldwide according to the World Health Organization (WHO).

The researchers have also recently boosted the comb breathalyser’s diagnostic power by expanding the spectral coverage to detect additional molecules, and plan to employ additional AI approaches such as deep learning to improve its disease-detection abilities. Efforts are already underway to miniaturise and simplify the technology to make it portable and easy to use in hospitals and other care settings.

Image caption: Qizhong Liang adjusts JILA’s frequency comb breathalyser. Image credit: R Jacobson/NIST.