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Increased humidity of building envelopes frequently leads to the appearance and growth of mold, which is one of the most important factors concerning Sick Building Syndrome evaluation. Detection of Volatile Organic Compounds (VOCs) emitted by the fungi can be performed using gas sensor arrays. Array output in a form of multidimensional electric signals has to be analyzed by means of appropriate statistical methods. The idea presented within this paper is to use Support Vector Machine (SVM) in the classification of the mold-infested buildings because of different admixture of fungal VOCs within their indoor atmosphere. The mappings used by SVM schemes are designed to ensure that dot products of pairs of input data vectors are computed in terms of the variables in the original space, by defining them in terms of a kernel function k(x,y) selected to suit the problem. Using different types of kernel function actual levels of contamination could be assessed based on readouts from a Metal Oxide Semiconductor (MOS) sensors array. SVM method is appropriated for situations where the functional form of the relationship between signals from sensor array and the classes of contamination is unknown or relationship is complex. The multidimensional sensor readouts pertaining to the air sampled near the building envelopes in varying degree of mold-contamination, compared with clean and synthetic air, are interpreted and presented.
