Breath analysis is an expanding scientific field with great potential for creating personalized and non-invasive health screening and diagnostics techniques. However, the wide range of contradictory results in breath analysis is explained by the lack of an optimal standard procedure for selective breath sampling. Recently we developed novel instrumentation for selective breath sampling, enabling the precise collection of a pre-determined portion of exhaled air using AI (Machine Learning) algorithm. This work presents pilot study results for validation of developed technology by differentiation of alveolar and oesophagal air obtained from the healthy population (n = 31). The samples were analyzed in-situ by Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) apparatus, and obtained spectra were processed with proper multivariate classification tools. The results show a promising performance of proposed AI-based technology for breath sampling adapted to users’ age, genre, and physiological conditions.