similarity in symptoms and expectations of _M. tuberculosis_ being dominant in endemic areas, patients are often mistakenly assumed to have multidrug-resistant tuberculosis when 5% and 30% of suspected cases are caused by NTM. The current, rapid nucleic acid amplification tests, such as GeneXpert(tm) decrease the diagnostic burden, but this test and others like it do not parse _Mycobacterium_ species, which are likely more broadly distributed than currently reported.  
Isolation of _Mycobacterium_ species from sputum, feces or tissue still represents the gold standard for the diagnosis of the family of _Mycobacteriaceae_[3], but due to the long generation time of some species, a complete diagnosis can take several weeks [4, 5]. Moreover, extra-time can be required to identify the specific _Mycobacterium_ species for a proper treatment. It is worthy to mention that matrix-assisted laser desorption/ionization (MALDI)-MS technology represents a rapid screening tool, faster than traditional microbiological techniques, capable of distinguishing at species level, even if a previous purification stage is normally required and the instrument represents an important economical investment.  
The analysis of volatile organic compounds (VCCs) produced from in vitro cultures and/or ex vivo specimens, represents a viable and cheaper alternative to non-invasive diagnose mycobacteria at the species level [6, 7], although large-scale investigations are still needed to validate the identity of the biomarkers [8]. In vitro studies represent an important and easier step to investigate VOCs produced by the specific bacterium under study, thus highlighting possible biomarkers. Nevertheless, these findings need to be then validated in ex vivo and in vivo scenarios. Different analytical techniques are commonly used in VOC analysis, including: sensor-based electronic noses [9, 10], microfluidic colorimetric assays [11], selected ion flow tube-mass spectrometry (SIFT-MS) [12], proton transfer reaction mass spectrometry (PTR-MS) [13], ion mobility spectrometry (IMS) with gas chromatographic pre-separation by a multi-capillary column (MCC) [14] and gas chromatography (GC) based techniques coupled to mass spectrometry (MS) [15, 16, 17]. The ultimate goal would be to transfer the high level of information acquired using the aforementioned instrumentations into an easy and straightforward point-of-care (POC) device to target the biomarkers identified.  
In this context, the aim of this work is to investigate the biomarker candidates produced by different mycobacteria species for their classification to contribute to the knowledge needed to develop a reliable POC device, and to provide a reference point for future validation of ex vivo and in vivo studies. Here, a simple, ready-to-use headspace solid-phase microextraction (HS-SPME) GC-MS analytical platform was applied to the putative identification of in vitro biomarkers among seven different mycobacteria species belonging to three different complexes. SPME has been used widely for the analysis of VOCs since its invention in the early 1990s [18, 19]. SPME is a simple and effective sample preparation technique which combines sampling, isolation and concentration in a single step. After preconcentration, VOCs from mycobacteria were analyzed by GC-quadrupole (q)MS. The detected analytes were treated with different data processing techniques and the discriminatory capability of the selected volatiles was evaluated. Chemometrics is a well-established aid in the discovery of differences between samples with many variables [20]. In this context, a random forest (RF) machine learning algorithm was applied to select a panel of discriminatory features able to distinguish among different mycobacteria species.