Preliminary assessment of a model to predict mold contamination based on microbial volatile organic compound profiles

Abstract

Identification of mold growth based on microbial volatile organic compounds (MVOCs) may be a viable alternative to current bioaerosol assessment methodologies. A feed-forward back propagation (FFBP) artificial neural network (ANN) was developed to correlate MVOCs with bioaerosol levels in built environments. A cross-validation MATLAB script was developed to train the ANN and produce model results. Entech Bottle-Vacs were used to collect chemical grab samples at 10 locations in northern NY during 17 sampling periods from July 2006 to August 2007. Bioaerosol samples were collected concurrently with chemical samples. An Anderson N6 impactor was used in conjunction with malt extract agar and dichloran glycerol 18 to collect viable mold samples. Non-viable samples were collected with Air-O-Cell cassettes. Chemical samples and bioaerosol samples were used as model inputs and model targets, respectively. Previous researchers have suggested the use of MVOCs as indicators of mold growth without the use of a pattern recognition program limiting their success. The current proposed strategy implements a pattern recognition program making it instrumental for field applications. This paper demonstrates that FFBP ANN may be used in conjunction with chemical sampling in built environments to predict the presence of mold growth.