The occurrence of disease amongst the occupants of "mouldy" environments has been widely described in the literature. However, the detection of such moulds in closed environments remains difficult, particularly in the event of recent (before the first deterioration) or masked contamination (behind a material). In this context, the present study aimed to determine a specific chemical fingerprint for fungal development detectable in closed environments (dwellings, office, museum...). To achieve this, chemical emissions from sterile and artificially contaminated by moulds materials were analyzed and compared using a descriptive statistical method. Principal Component Analysis is thus chosen to analyze the results. PCA generated optimum and similar graphical representations of the scatterplot representing the data matrix. This statistical approach made it possible to identify an emission fingerprint without applying any preconception as to the type of emitted compound. Statistical analysis of the data then enabled confirmation of the impact of moulds on total VOC emissions. This emission of specific compounds resulted in obtaining a signature for the presence of fungal development in an environment, defined by specific ions. This analysis, and use of these ions applied to dwellings, made it possible to distinguish those with proven fungal development from those with no sign of mould or with a context favorable to fungal development, thus demonstrating that a chemical fingerprint specific to fungal development could be detected in indoor environments.