Expiratory flow rate, breath hold and anatomic dead space influence electronic nose ability to detect lung cancer

BACKGROUND: Electronic noses are composites of nanosensor arrays. Numerous studies showed their potential to detect lung cancer from breath samples by analysing exhaled volatile compound pattern ("breathprint"). Expiratory flow rate, breath hold and inclusion of anatomic dead space may influence the exhaled levels of some volatile compounds; however it has not been fully addressed how these factors affect electronic nose data. Therefore, the aim of the study was to investigate these effects. METHODS: 37 healthy subjects (44 +/- 14 years) and 27 patients with lung cancer (60 +/- 10 years) participated in the study. After deep inhalation through a volatile organic compound filter, subjects exhaled at two different flow rates (50 ml/sec and 75 ml/sec) into Teflon-coated bags. The effect of breath hold was analysed after 10 seconds of deep inhalation. We also studied the effect of anatomic dead space by excluding this fraction and comparing alveolar air to mixed (alveolar + anatomic dead space) air samples. Exhaled air samples were processed with Cyranose 320 electronic nose. RESULTS: Expiratory flow rate, breath hold and the inclusion of anatomic dead space significantly altered "breathprints" in healthy individuals (p 0.05). These factors also influenced the discrimination ability of the electronic nose to detect lung cancer significantly. CONCLUSIONS: We have shown that expiratory flow, breath hold and dead space influence exhaled volatile compound pattern assessed with electronic nose. These findings suggest critical methodological recommendations to standardise sample collections for electronic nose measurements

Evening and morning exhaled volatile compound patterns are different in obstructive sleep apnoea assessed with electronic nose

PURPOSE: Electronic noses represent a technique for the measurement of exhaled breath volatile compound pattern which can discriminate patients with obstructive sleep apnoea (OSA) from control subjects. Although overnight changes in circulating biomarkers were reported, this effect on the exhaled volatile compound pattern has not been studied before. We aimed to compare breath patterns in the evening and in the morning in patients with OSA and to study the ability of the electronic nose to distinguish patients from controls based on these exhaled volatile patterns. METHODS: Exhaled breath volatile compound pattern was measured before and after night in 26 patients with suspected sleep-disordered breathing (53 +/- 15 years) who underwent polysomnography and in ten control subjects (37 +/- 15 years), by whom sleep-disordered breathing was excluded with a home apnoea screening device. Breath measurements were also performed in the morning in 26 healthy, non-smoking age-matched controls (48 +/- 10 years) with no complaints about disturbed sleep. Exhaled volatile compound pattern was processed with a Cyranose 320 electronic nose, and principal component analysis was used for statistical analysis. RESULTS: Exhaled volatile compound patterns recorded in the evening and in the morning were different in patients with OSA (p = 0.01) but not in non-OSA habitual snorers (p = 0.49) or in control subjects (p = 0.23). The electronic nose distinguished patients with OSA from control subjects based on the breath samples collected in the morning (p 0.05). CONCLUSIONS: Evening and morning exhaled volatile compound patterns are different in OSA. This might affect the ability of electronic noses to identify this disorder. Overnight alterations in volatile substances need to be taken into account during exhaled breath measurements in OSA