Deep learning method to remove chemical, kinetic and electric artifacts on ISEs

Abstract

This paper presents a signal-processing method to remove pan-artifact on ISEs with artificial neural networks. An Ion Selective Electrode 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 multi-ion solution has problem. First problem is a chemicophysical 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 that movement of liquid directly interfere the glass membrane, causing inaccurate voltage measurement. When multiple ISEs are dipped into same solution, a sensor's signal emission interference voltage measurement of other sensors. Therefore, an ISE is recommended to applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both artifacts at the same time. The proposed method is designed to remove complex artifacts with one-shot calculation, with MAPE less than 1.8%, and R 2 as 0.997. A randomly chosen value of AI-predicted value has MAPE less than 5% (p-value 0.016).