Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography – Time of flight mass spectrometry and machine learning

• Published in: Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Vol. 1074-1075; pp. 46 - 50
• Main Authors: Beccaria, Marco, Mellors, Theodore R., Petion, Jacky S., Rees, Christiaan A., Nasir, Mavra, Systrom, Hannah K., Sairistil, Jean W., Jean-Juste, Marc-Antoine, Rivera, Vanessa, Lavoile, Kerline, Severe, Patrice, Pape, Jean W., Wright, Peter F., Hill, Jane E.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2018
• Subjects: Adolescent; Adult; Breath analysis; Breath Tests - methods; Comprehensive two-dimensional gas chromatography; Female; Gas Chromatography-Mass Spectrometry - methods; Humans; Machine Learning; Male; Middle Aged; Pulmonary tuberculosis; Tuberculosis - diagnosis; Volatile organic compounds; Volatile Organic Compounds - analysis; Volatile Organic Compounds - chemistry; Young Adult; Volatile organic compounds; Comprehensive two-dimensional gas chromatography; Breath analysis; Pulmonary tuberculosis; Machine learning
• ISSN: 1570-0232; 1873-376X; 1873-376X
• DOI: 10.1016/j.jchromb.2018.01.004

Tuberculosis (TB) remains a global public health malady that claims almost 1.8 million lives annually. Diagnosis of TB represents perhaps one of the most challenging aspects of tuberculosis control. Gold standards for diagnosis of active TB (culture and nucleic acid amplification) are sputum-dependent, however, in up to a third of TB cases, an adequate biological sputum sample is not readily available. The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, and non-invasive, and ready-available diagnostic service that could positively change TB detection. Human breath has been evaluated in the setting of active tuberculosis using thermal desorption-comprehensive two-dimensional gas chromatography–time of flight mass spectrometry methodology. From the entire spectrum of volatile metabolites in breath, three random forest machine learning models were applied leading to the generation of a panel of 46 breath features. The twenty-two common features within each random forest model used were selected as a set that could distinguish subjects with confirmed pulmonary M. tuberculosis infection and people with other pathologies than TB. •For the first time a GC×GC system was used to evaluate human breath in the setting of tuberculosis disease•Three random forest models were applied for data elaboration•Twenty-two common features within each random forest model used were selected as a set to distinguish between infected and control group