Fungal finder: Microbial volatiles predict extent of contamination

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  • Published: Aug 27, 2012
  • Author: Steve Down
  • Channels: Gas Chromatography
thumbnail image: Fungal finder: Microbial volatiles predict extent of contamination

Something in the air

A combination of VOC profiling and spore counting has been used to develop a method for predicting the degree of indoor mould contamination based on pattern recognition analysis of compounds in the GC/MS profile.


The presence of mould indoors can lead to asthma and other respiratory problems, so its reliable detection is essential for suspect properties that are inhabited by residents or workers. The established commercial methods involve trapping the spores from the air or wiping them from surfaces, but they might miss moulds that are growing behind walls or beneath wall coverings, since the spores cannot pass through building materials.

An alternative method for mould detection gets round this limitation by targeting the volatile organic compounds (VOCs) that are emitted by moulds. VOCs can diffuse through many types of building material, which is often why people can smell the characteristic musty odour before they can see the mould. Both of these methods, spore trapping and VOC analysis, only provide a snapshot of the state of a room at a particular moment in time, but the latter method has shorter sampling times.

In addition, VOC analysis allows a group of compounds to be measured, rather than only one, which will lead to a more reliable prediction of the presence of mould. The detection of moulds from their VOCs is an active area of research and one new project has attempted to validate the used of VOCs against the measurement of spores.

Barry K. Lavine and Nikhil Mirjankar from Oklahoma State University, Stillwater, OK, with Ryan LeBouf and Alan Rossner from Clarkson University, Potsdam, NY, used GC/MS to study the composition of the air in mouldy environments and correlated it with the mould count.

Airborne mould volatiles

In the first instance, the researchers grew Aspergillus niger or a mixed mould in Petri dishes and collected the resulting VOCs from the headspace above the dishes by SPME. The adsorbed gases were analysed by GC/MS and the peak areas were extracted from the chromatograms for pattern recognition analysis using a commercial suite of programs and a genetic algorithm for discrimination.

A principal components analysis of the GC data revealed that the mixed mould was well separated from A. niger using two principal components. When the genetic algorithm was applied to the GC peaks that are characteristic of each mould, nine GC peaks were found that classified the moulds in the principal components plot. So, discrimination on the basis of VOCs worked in the lab.

Then, the team took the method into the field and collected spores and VOCs concurrently from 16 locations in Potsdam, NY. The spore counts placed 43, 14 and 42 samples into low, medium and high density classes for both of the collection media that were used.

For the first spore collection medium, which was dichloran glycerol 18, the peaks from the 145 gas chromatograms were poorly resolved in the pattern recognition analysis. The plots showed considerable overlap between the low and medium mould counts and between the medium and high counts. The results were far more promising for the second collection medium, a malt extract agar. Here, the same GC profiles showed a better correlation to the spore counts.

More than 120 of the chromatograms were used as training sets and each was analysed by linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and back propagation neural networks (BPNN) then used to classify the other chromatograms.

Each of the three methods performed well, with BPNN the best at an average success rate of 90.2% compared with 84.2 and 78.9% for LDA and QDA, respectively. The high mould count chromatograms were distinguished the best, indicating that a specific VOC profile was recognised by the algorithms. For the medium and low count chromatograms, some were misclassified as each other, probably due to interference from other volatiles in the air from cooking, cleaning and the like.

The new method could be used to predict the fungal load of contaminated rooms from the VOC profile emitted by moulds, placing the high loads with almost absolute certainty and the low-to-medium loads with a reasonable accuracy. This is a good outcome because it is environments with a high level of fungal contamination which are the most dangerous, so immediate action can be taken to rid the rooms of the mould.

Related Links

Microchemical Journal 2012, 103, 37-41: "Prediction of mold contamination from microbial volatile organic compound profiles using solid phase microextraction and gas chromatography/mass spectrometry"

Article by Steve Down

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

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