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Related Experiment Video

Updated: Jun 11, 2025

Breath Collection from Children for Disease Biomarker Discovery

Breath Collection from Children for Disease Biomarker Discovery

Published on: February 14, 2019

COVID-19 detection from exhaled breath.

Nicolò Bellarmino¹, Riccardo Cantoro², Michele Castelluzzo³

¹Politecnico di Torino, Torino, Italy. nicolo.bellarmino@polito.it.

Scientific Reports

|October 6, 2024

Summary

A new, non-invasive COVID-19 detection system uses exhaled breath analysis. This rapid breath test shows accuracy comparable to PCR and antigen tests for identifying SARS-CoV-2 infection.

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Last Updated: Jun 11, 2025

Breath Collection from Children for Disease Biomarker Discovery

Breath Collection from Children for Disease Biomarker Discovery

Published on: February 14, 2019

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Published on: March 9, 2018

Fast and Accurate Exhaled Breath Ammonia Measurement

Fast and Accurate Exhaled Breath Ammonia Measurement

Published on: June 11, 2014

Area of Science:

Biomedical Engineering
Infectious Disease Diagnostics
Analytical Chemistry

Background:

The COVID-19 pandemic necessitated rapid development of diagnostic tools.
Existing methods like PCR and antigen tests have limitations in speed and invasiveness.
Monitoring and reducing infection rates remain critical public health priorities.

Purpose of the Study:

To introduce a novel, cost-effective, and non-invasive COVID-19 detection system.
To evaluate the performance of a breath-based diagnostic system for SARS-CoV-2.
To explore the potential of machine learning in analyzing breath mass spectra for disease detection.

Main Methods:

Utilized a micro/nano-sampling device coupled with a high-precision spectrometer to analyze exhaled breath mass spectra (10-351 m/z).
Employed custom software algorithms for raw spectra processing and data augmentation.
Applied state-of-the-art machine learning algorithms for classification of COVID-19 cases.

Main Results:

The system demonstrated high performance in an uncontrolled clinical trial (302 subjects).
Achieved accuracy of 95%, recall of 94%, specificity of 96%, and F1-score of 92%.
Performance was comparable to traditional polymerase-chain-reaction and antigen testing.

Conclusions:

The proposed breath analysis system offers a rapid and non-invasive alternative for COVID-19 detection.
This technology has the potential for significant contributions to routine screenings and epidemic response.
Further development could enhance capabilities for future public health emergencies.