Volterra System Analysis for an Electrochemical Sensor

Abstract

Current biological methods for quantifying bacterial and fungal populations are time and labour intensive, whilst remaining expensive to automate. A potential solution to this problem is an electrochemical sensor, which applies a stochastic voltage across a liquid medium and measures the resultant current flow. This data can then be used to model the liquid’s electrochemical interactions and monitor it for bacterial growth and spoilage. Linear dynamic impedance models have previously been explored for this. However, the ability to capture the nonlinear effects observed at higher voltages can provide greater insights into the liquid’s properties. This is extremely difficult with neural networks which offer accurate predictive capabilities without much insight into the system. A different strategy is to model the liquid using a Volterra series representation. This work will document the integration of Volterra system identification capabilities within the sensor and its performance when modelling different liquid media as well as modifications made to the sensor for the applications tested.