Evaluation of VOCs from fungal strains, building insulation materials and indoor air by solid phase microextraction arrow, thermal desorption–gas chromatography-mass spectrometry and machine learning approaches

Abstract

Solid phase microextraction Arrow and thermal desorption–gas chromatography–mass spectrometry allowed the collection and evaluation of volatile organic compounds (VOCs) emitted by fungal cultures from building insulation materials and in indoor air. Principal component analysis, linear discriminant analysis and supported vector machine were used for visualization and statistical assessment of differences between samples. In addition, a screening tool based on the soft independent modelling of class analogies (SIMCA) was developed for identification of fungal contamination of indoor air. Ten different fungal strains, incubated under ambient and microaerophilic conditions, were analyzed for time period ranging from 5 to 29 days after inoculation resulting in a total of 140 samples. In addition, the effect of additives on the fungal growing media was studied. The total number of compounds and concentration values were used for the evaluation of the results. Clear differences were observed for VOC profiles emitted by different fungal strains by exploiting long chain alcohols (3-octanol, 1-hexanol and 2-octen-1-ol) and sesquiterpenes (farnesene, cuprene). The analysis of glass-wool and cellulose based building insulation materials (3 samples) gave clear differences, mainly for oxygenated compounds (ethyl acetate and hexanal) and benzenoids (benzaldehyde). Moreover, the comparison of indoor air and insulation materials collected from a house with fungal indoor air problems indicated that 42% of the VOCs were found in both samples. The analysis of 52 indoor air samples demonstrated clear differences in their VOC profiles, especially for hydrocarbons, and between control (44 samples) and indoor air problem houses (8 samples). Finally, the SIMCA model enabled to recognize differences between control and fungi contaminated houses with a prediction capacity over 84%.