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.
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García, E.R., Burtseva, L., Stoytcheva, M., González, F.F. (2011). Predicting the Behavior of the Interaction of Acetylthiocholine, pH and Temperature of an Acetylcholinesterase Sensor. In: Batyrshin, I., Sidorov, G. (eds) Advances in Artificial Intelligence. MICAI 2011. Lecture Notes in Computer Science(), vol 7094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25324-9_50
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DOI: https://doi.org/10.1007/978-3-642-25324-9_50
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