Application of a Neural Network for the Simultaneous Identification of Several Analytes

Competition chemiluminescent immunoassay was used in a combination with a neural network (NN) to identify and estimate amounts of three cross-reacting s-triazines (atrazine, terbythylazine and ametryn). Antibodies with different cross-reactivity towards s-triazines were immobilized in separate wells of 8-well microtitre strip or in separate spots of a single membrane strip. The data obtained with chemiluminescent ELISA and membrane immunoassay were processed by NN. The main objective for NN application was to find the best topology, learning method and its parameters for the correct estimation of the amount, as well as, the correct identification of an individual compound in a mixture. The necessity of additional normalization for native experimental data was examined. The correct s-triazine classification of environmental samples containing various analyte mixtures was possible in 74–92% of all cases depending on the type of analyte. The test developed can be proposed as an alternative field test for multianalyte environmental monitoring.