A method for early detection and identification of fungal contamination of building materials using e-nose

Abstract

The aim of the study was to develop a method for early detection and identification of fungal contamination of building materials using an electronic nose. Therefore, the laboratory experiments based on the analysis of the air in the vicinity of fungal isolates potentially found in the building materials were performed. The results revealed that the employed gas sensors array consisting of MOS-type sensors enables the detection of the differences among the examined samples of fungi and distinguishing between the non-contaminated and contaminated samples, shortly after fungal contamination occurs. Electronic nose readouts were analysed using Principal Component Analysis and the results were verified with standard chromatographic analysis by means of SPME-GC/MS method, which proved that gas sensors array can be applied for early detection of fungal contamination.

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

“…Another fungal infection of strawberries was reported by Greenshields et al [ 23 ]. Detection by the electronic nose of fungal contamination of wood has been reported [ 24 , 25 ]. Other authors reported studies of detection of fungal infection of various grains [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”

An Outlook of Recent Advances in Chemiresistive Sensor-Based Electronic Nose Systems for Food Quality and Environmental Monitoring

“…Contamination of building materials by microbes is another environmental issue addressed by researchers. Suchorab et al developed an Enose using eight commercial SMO based gas sensors to detect various microbial VOCs released by ten different genres of fungi [124]. Using PCA in conjugation with chromatographic analysis, they concluded that their sensor array was able to differentiate between contaminated and non-contaminated samples and also identify individual fungi genera after 3 h of inoculation, whereas chromatography was able to differentiate between different genres of fungi only after 72-120 h of fungal growth.…”

Methods for Early Detection of Microbiological Infestation of Buildings Based on Gas Sensor Technologies

“…In numerous cases, this value is below the limit of detection of the utilized sensors. Direct measurements conducted in rooms, especially by means of an e-nose, may be used for a general and preliminary evaluation of the fungal infestation of a building [89,139]. In such cases, clear symptoms of infestation significantly exceeding the standard values can already be observed.…”

“…Principal component analysis and cluster analysis are commonly used for visualization of multidimensional observations in space and unsupervised classification [98,139,177,186]. In turn, the supervised learning methods can be employed to confirm the hidden structure (i.e., homogenous clusters of data) for the purpose of classification.…”