Screening GC-MS data for carbamate pesticides with temperature-constrained–cascade correlation neural networks Original Research Article

Aromatic carbamate pesticides are important agrochemicals. Mass spectral classification models were built for carbamates and their substructures using temperature-constrained–cascade correlation networks (TC–CCNs). The carbamate classifier was applied to the mass spectral scans of a GC-MS run. The classification models were built from reference and experimental mass spectra. Different network configurations were compared that used multiple network models with single outputs and single networks with multiple outputs. A major source of variation caused by randomly partitioning the training and prediction sets was reduced by an order of magnitude by using a method of Latin-partitions. This method also furnished a precision measure for comparing classification methods. Multiple networks with single outputs generally predicted better than single networks with multiple outputs. Hierarchical single output networks achieved better than 98% classification accuracy in one study. The TC–CCN models compared favorably to the K-nearest neighbors (KNN) and discriminant partial least squares (DPLS) reference methods.