Abstract
Abstract Fitting the data of an electrochemical impedance spectroscopy (EIS) typically requires manual estimation of the initial values before regression algorithms such as complex nonlinear least squares (CNLS) can be applied. This makes the success rate of the fitting dependent on the user input. Furthermore, the Randles circuit consists of parameters with substantially differing magnitudes (e.g. capacitors and resistors), which can also strongly affect the success rate of the fitting due to numerical effects. The aim of this work is to investigate methods addressing the described limitations of fitting. Therefore, a Python implementation performing a fit for EIS is benchmarked with an equivalent open source library. The examined implementation optionally includes the normalization of the parameter values, the standardization of the impedances and a pre-fit. Applying the same equivalent circuit without additional signal processing steps and with fixed initial values defined by midpoints of the value ranges, both implementations were able to fit 46.50% of the simulated database with different spectra. Applying the normalization of the parameter values (76.25%) or the same method with additional pre-fit (97.75%) lead to a significant improvement of the success rate. The standardization of impedances did not affect the success rate.
Highlights
Electrochemical impedance spectroscopy (EIS) is a method allowing chemical and physical phenomena on the biosensor surface to be examined separately in the context of biorecognition processes
The results indicate that impedance.py and EIS:CReME give almost identical results, which is to be expected since both use the same Python method for fitting
The normalization of the parameter values resulted in a significant improvement of the success rate, the root mean square relative error (RMSRE) and χ2
Summary
Electrochemical impedance spectroscopy (EIS) is a method allowing chemical and physical phenomena on the biosensor surface to be examined separately in the context of biorecognition processes. Initial estimations for the parameters to be fitted must be typically provided manually before applying a complex nonlinear least squares (CNLS) algorithm This makes the success rate of the fitting dependent on the user input, since there is the possibility to get stuck in a local optimum. The input of manually determined start values is unsuitable for systems with automatic evaluation of the EIS data, if a wide range of parameter values can occur. Another peculiarity is that the parameters of the equivalent circuit have enormous differences in magnitude, since resistors and capacitors are used. This in turn can lead to numerical effects that influence the success rate of a fit
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