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A technique for screening mass spectra for the presence of volatile organic compounds (VOCs) is developed using probabilistic neural networks. A parallel neural network filter is designed to recognize benzene, toluene, ethyl benzene, and o-xylene in gas chromatography—mass spectrometry (GC–MS) chromatograms of VOC mixtures. The filter trained rapidly and was evaluated by analyzing a variety of VOC combinations. The performance of the network offers some significant advantages over the traditional GC–MS data processing techniques such as ion extraction and compound library searching. Advantages include speed, selectivity, and the ability to discriminate between overlapping compounds.

A Fast Mass Spectrum Screening Technique for Volatile Organic Compounds Based on Parallel Artificial Neural Networks