Volatile Organic Compound Analysis in Disease Diagnosis

Volatile organic compounds (VOCs) represent a diverse class of small molecules generated by endogenous metabolism, cellular stress and microbial activity. Their presence in exhaled breath, headspace above biological fluids or tissue culture, and other accessible biological samples offers a non-invasive window into physiological and pathological processes. Advanced analytical platforms—including mass spectrometry, ion mobility spectrometry and mid-infrared spectroscopy—enable detection of trace VOCs, while machine learning facilitates pattern recognition across complex datasets. Applications span oncology, respiratory and infectious diseases, and metabolic disorders, with promise for early detection, prognosis, treatment monitoring and point-of-care screening. Progress hinges on overcoming challenges in sample standardisation, control of confounding factors and validation across diverse populations.

Research from Nature Portfolio

Recent studies have demonstrated a hybrid sensing approach that integrates artificial intelligence-enhanced ion mobility spectrometry with mid-infrared spectroscopy to achieve precise VOC identification. By harnessing a triboelectric generator to produce a cold plasma discharge, researchers amplified spectroscopic responses and addressed limitations of conventional ion mobility under restrictive conditions. Multidimensional signal features are analysed by machine learning algorithms, yielding over 99% accuracy in quantifying isopropyl alcohol amid complex carbon-based gas interferences. This synergistic platform exemplifies a new paradigm in non-invasive gas sensing with potential application to a broad spectrum of disease-related VOC biomarkers.

Volatile Organic Compound Analysis in Disease Diagnosis publication trend

The graph below shows the total number of articles in volatile organic compound analysis in disease diagnosis across all publications each year (not limited to Nature Index journals).

Technical terms

Volatile Organic Compounds (VOCs): Organic molecules with sufficient vapour pressure to partition into the gas phase at ambient temperature, serving as biomarkers of metabolic and pathological states.

Ion Mobility Spectrometry (IMS): Separation technique that differentiates gas-phase ions by their drift velocity in an electric field, enabling rapid analysis of complex mixtures.

Mid-Infrared Spectroscopy (MIR): Analytical method that probes molecular vibrations via absorption of mid-infrared light, yielding characteristic spectral fingerprints of chemical bonds.

Mass Spectrometry (MS): Technique that ionises analytes and measures their mass-to-charge ratios for molecular identification and quantification.

Solid-Phase Microextraction (SPME): Solvent-free sampling approach that concentrates volatile analytes onto a coated fibre prior to instrumental analysis.

Electronic Nose (e-nose): Sensor array system that mimics olfaction, detecting and discriminating complex VOC mixtures through pattern recognition algorithms.

Breathomics: Comprehensive profiling of exhaled breath constituents to capture metabolic signatures associated with health and disease.

Random Forest Algorithm: Ensemble machine learning method that constructs multiple decision trees to perform robust classification and regression tasks.

References

1. Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy. Nature Communications (2023).
2. A novel non-invasive exhaled breath biopsy for the diagnosis and screening of breast cancer. Journal of Hematology & Oncology (2023).
3. Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform. Theranostics (2024).
4. Exhaled breath analysis: a review of ‘breath-taking’ methods for off-line analysis. Metabolomics (2017).