Predicting the behavior of the interaction of acetylthiocholine, pH and temperature of an acetylcholinesterase sensor

Abstract

The steady-state current response of an acetylcholinesterase electrochemical sensor of second generation, which results from the interaction of substrate concentration, pH and temperature, was evaluated to improve biosensor's analytical characteristics using computational learning models. Artificial Neural Network and Support Vector Machine models demonstrated excellent results, despite of the limited number of samples. The predictions provided by both models were compared in order to determine which of them possesses a better approximation of the response generated by the sensor signal. © 2011 Springer-Verlag.