Artificial neural networks methods applied to conductometric microhotplate data for the identification of the type and relative concentration of chemical warfare agents

Response data from microhotplate (MHP) sensor arrays were measured for various chemical warfare (CW) agents in several concentrations in dry hair. Efficient large-scale artificial neural networks (ANN) modeling has been evaluated as a method for the classification and concentration prediction of the CW agents based on the MHP data. Four MHP sensor elements, two pairs of SnO₂ and two pairs of TiO₂ were operated in a pulsed, ramped temperature mode to generate the data used. The CW agents and related compounds tested were tabun (GA), sarin (GB), sulfur mustard (HD), and chloroethyl-ethyl-sulfide (CES), in four concentration levels in dry hair, between several nmole/mole (ppb) to several μmole/mole (ppm). Recursive ANN pruning and re-training techniques were used to identify the more relevant inputs, among the original 80 inputs (different sensor elements and temperatures). ANN models with 6-15 inputs produced good classification between the different CW agents. Other ANN models, trained for each agent, gave good prediction values for the concentrations of the CW agents.