  In the tasks of environmental monitoring is of great importance to have
compact and portable systems able to identify environmental contaminants that
facilitate tasks related to waste management and environmental restoration. In
this paper, a prototype sensor is described to identify contaminants in the
environment. This prototype is made with an array of tin oxide SnO2 gas sensors
used to identify chemical vapors, a step of data acquisition implemented with
ARM (Advanced RISC Machine) low-cost platform (Arduino) and a neural network
able to identify environmental contaminants automatically. The neural network
is used to identify the composition of contaminant census. In the computer
system, the heavy computational load is presented only in the training process,
once the neural network has been trained, the operation is to spread the data
across the network with a much lighter computational load, which consists
mainly of a vector-matrix multiplication and a search table that holds the
activation function to quickly identify unknown samples.
