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Mold is considered to be one of the most important features of Sick Building Syndrome (SBS) and is an important problem in building sector. In numerous cases it is caused by the rising moisture of building envelopes and overstated humidity of indoor air. Fungal contamination is normally evaluated using standard biological methods which are time-consuming and require a lot of manual labor. But also, fungi emit Volatile Organic Compounds (VOC) that can be detected in the indoor air using several techniques of detection e.g. chromatography. VOCs can be also detected using gas sensors arrays. All arrays of sensors generate particular electric signals that ought to be analyzed using statistical methods of interpretation. This work is focused on the attempt to apply unsupervised and supervised statistical classifying models in the evaluation of signals from gas sensors array to analyze the air from various types of the buildings. Basing on our research there is proposed buildings mold threat evaluation using MOS (Metal Oxide Semiconductor) sensors array. Presented results show the interpretation sensors readouts of indoor air sampled in lodgings threatened with mold development in comparison with clean reference one and synthetic air.
