High Concordance of E-Nose-Derived Breathprints in a Healthy Population: A Cross-Sectional Observational Study

Highlights: What are the main findings? E-nose breathprints in healthy subjects exhibited high concordance, with 138 out of 139 individuals clustering closely together, demonstrating low variability. Principal Component Analysis (PCA) explained 97.15% of the variance, reinforcing the stability of e-nose-based breath signatures under controlled conditions. What is the implication of the main finding? This study supports the feasibility of e-nose standardization, as breathprints from healthy individuals are reproducible and show minimal variability. Findings pave the way for clinical applications, suggesting that e-nose technology could reliably differentiate between normal and pathological breath signatures. Exhaled breath analysis using electronic noses (e-noses) is a promising non-invasive diagnostic tool. However, a lack of standardized protocols limits clinical implementation. This study evaluates the consistency of breathprints in healthy subjects using the Cyranose 320 e-nose to support standardization efforts. Breath samples from 139 healthy non-smoking subjects (age range 18–65 years) were collected using a standardized protocol. Participants exhaled into a Tedlar bag for immediate analysis with the Cyranose 320. Principal Component Analysis (PCA) was used to reduce data dimensionality, and K-means clustering grouped subjects based on breathprints. PCA identified four principal components explaining 97.15% of variance. K-means clustering revealed two clusters: 1 outlier and 138 subjects with highly similar breathprints. The median distance from the cluster center was 0.21 (IQR: 0.18–0.24), indicating low variability. Box plots confirmed breathprint consistency across subjects. The high consistency of breathprints in healthy subjects supports the feasibility of standardizing e-nose protocols. These findings highlight the potential of e-noses for clinical diagnostics, warranting further research in diverse populations and disease cohorts.