Abstract:The paper presents a review of several studies on the detection of microbial volatile organic compounds (MVOCs) considered as indicators of fungal contamination. As fungi produce specific profiles, or fingerprints of volatile compounds, the electronic nose technology is a very promising opportunity for rapid and non costly detection of fungi in buildings. E-noses are able to distinguish between mouldy and non-mouldy samples, and also to recognise certain species of fungi. However, two limiting factors may appear decisive for employment of electronic noses in indoor fungi detection: low concentrations of MVOCs and presence of interfering substances in indoor environments.

In this paper, the monitoring of the composting process with an e-nose is presented. An emission chamber is developed for this purpose and put on a household waste compost pile. A lab-made e-nose with metal oxide sensors is located at the exit of this chamber. Simultaneously to the e-nose measurements, air sampling on sorbent tubes as well as physico-chemical analysis are realised. The adsorbed air samples are analysed in the lab by gas chromatography coupled to mass spectrometry (GC-MS). In addition, some parameters of the composting process are collected (compost temperature, age of the pile, date of the aeration). Correlation between the sensors and 14 chemical families is determined by principal component analysis (PCA). By canonical analysis, two models are developed and calibrated by the proportion of each chemical family and in function of the compost process events. Thanks to these models, monitoring of various kinds of compost process events is possible with only one measurement device.

This work explores the detection of moulds growing in different building materials by using a metal oxide sensor array. Four moulds species have been considered. Pattern classification provides detection rates on the order of 80-85% for different species. Drift degrades only slightly these values subsequent test 4 months later.

The possibility to detect Aspergillus versicolor growing on different building materials by a metal oxide sensor array is studied. Results show that an accurate classification rate of 89 ± 3% can be obtained combining an extended linear discriminant analysis plus a fuzzy k-NN classifier. The classification ability of the classifier is assessed within the dataset by cross validation and also in a second dataset collected 5 months later. There is a slight decrease in the classification performance for all the algorithms, being the most sensitive the most accurate one.

This review deals with environmental home inspection services in Western Europe provided for patients at the request of attending physicians to improve patient management. Such requests are usually motivated by respiratory or general symptoms which occur or worsen at home. The visit includes a standardised questionnaire as well as environmental sampling such as mite-allergen measurement, mould identification and volatile organic compound (VOC) measurements. Besides, some nonrespiratory indoor risks are also taken into account. Following the visit, a report is sent to the family and the attending physician. These services have been developed since the early 1990s, but evaluation of their efficacy is still limited. Some studies have demonstrated a reduction in mite-allergen levels and clinical improvement following the visit and implementation of advice provided to the family. However, more studies are needed to further document efficacy and also perform cost-benefit analysis of these services.