  We suggest a deep learning based sensor signal processing method to remove
chemical, kinetic and electrical artifacts from ion selective electrodes'
measured values. An ISE is used to investigate the concentration of a specific
ion from aqueous solution, by measuring the Nernst potential along the glass
membrane. However, application of ISE on a mixture of multiple ion has some
problem. First problem is a chemical artifact which is called ion interference
effect. Electrically charged particles interact with each other and flows
through the glass membrane of different ISEs. Second problem is the kinetic
artifact caused by the movement of the liquid. Water molecules collide with the
glass membrane causing abnormal peak values of voltage. The last artifact is
the interference of ISEs. When multiple ISEs are dipped into same solution, one
electrode's signal emission interference voltage measurement of other
electrodes. Therefore, an ISE is recommended to be applied on single-ion
solution, without any other sensors applied at the same time. Deep learning
approach can remove both 3 artifacts at the same time. The proposed method used
5 layers of artificial neural networks to regress correct signal to remove
complex artifacts with one-shot calculation. Its MAPE was less than 1.8% and R2
of regression was 0.997. A randomly chosen value of AI-processed data has MAPE
less than 5% (p-value 0.016).
