Detecting wood-decay fungi in walls: a non-destructive approach with an electronic nose

Wood-decay fungi degrade the structural integrity and safety of wooden buildings, leading to potential hazards and reduced durability. Conventional detection methods, such as visual inspection and destructive sampling, are often impractical for concealed spaces, such as those within walls or beneath floors. These methods often require specialised training and costly equipment, making non-destructive alternatives highly desirable. This study explores an electronic nose (e-nose) as a non-destructive tool for detecting wood decay in a controlled wall model under different insulation conditions. The e-nose system, equipped with a semiconductor gas sensor array, analysed air samples from non-insulated, extruded polystyrene (XPS)-insulated, and glass fibre-insulated test specimens. Decay was induced using Fomitopsis palustris, while control specimens remained unexposed. Sensor responses were analysed using principal component analysis (PCA). The results demonstrated that decay-induced odour changes were distinguishable, with decayed and control specimens forming separate clusters in the PCA plot. However, in glass fibre-insulated models, some decayed samples overlapped with controls, potentially due to sensor drifts. These findings suggest that e-noses show potential for non-destructive wood decay detection, even in insulated structures. However, further validation is required to assess its generalisability under varying conditions. While sensor response patterns indicate the feasibility of decay detection, integrating machine learning approaches could enhance classification accuracy and robustness. Future research should explore algorithmic techniques to improve detection performance and expand the applicability of this method across different environmental conditions.