Prediction of substructure and toxicity of pesticides with temperature constrained-cascade correlation network from low-resolution mass spectra.

Abstract

Artificial neural networks are trained to predict the toxicity or active substructures of organophosphorus pesticides and then are applied to screening GC/MS data for environmentally hazardous compounds. Every mass spectral scan in the chromatographic run is classified, and separate chromatograms are obtained for either toxicity or substructure classes. Classification of mass spectra allows the detection of chromatographic peaks from potentially hazardous compounds that may be missing from the reference database. The neural network models predict substructures and toxicity from mass spectra without first determining the complete configurational structure of the pesticides. Temperature constrained-cascade correlation networks (TCCCN) were used because they are self-configuring networks that train rapidly and robustly. The toxicity classes are defined by the World Health Organization, and the substructure classes are standard organophosphorus pesticide groupings. The TCCCN models are used to mathematically resolve peaks in the chromatograms by substructure and toxicity. Evaluations yielded classification rates of 97 and 84% for substructure and toxicity, respectively.