Fast Nonlinear Dynamic Electrochemical Characterization of Arbitrary Fluids

An electrochemical sensor extracts information from biofluid systems by harnessing a nonlinear dynamic electrochemical model and stochastic voltage or current input. It uses a black-box approach that describes the fluid’s state and predicts its evolution over time using a collection of model parameters, nonlinear dynamic measurement modes, and modeling techniques. For example, the sensor can use principal component analysis to reduce the set of potentially hundreds of model parameters to a handful of latent variables that evolve independently of each other. The sensor can use a set of these latent variables as a description of the state of the fluid. For a given sample fluid (e.g., milk containing contaminants), the sensor collects trajectories of the fluid state over time under varying conditions, permitting the training of a machine learning model to predict either fluid state trajectories or the time until the fluid state crosses a given threshold (e.g., spoilage).