Square-wave voltammetry (SWV) is a widely used electrochemical technique for sensitive, temporally resolved measurement of redox reporter signals in biosensing applications. The Adaptive Square-Wave Voltammetry Iterative Fitting Toolkit (ASWIFT) is an automated analysis method developed to extract SWV signals robustly and in real time. ASWIFT uses an iterative, regularized smoothing framework to adaptively estimate baseline, select regularization parameters, fit the redox peak, and report a single, trace-level peak height without requiring manual, trace-specific parameter tuning.
Automated quantification of SWV traces is particularly challenging for long-duration recordings and for in vivo measurements. The authors identify several recurring problems that hinder robust automated analysis: changing baselines over time, heterogeneous and nonstationary noise, peak drift, sporadic outliers, and interfering faradaic processes. These complications can distort peak shapes and heights, producing biased or inconsistent estimates when standard, nonadaptive methods are applied.
ASWIFT is based on an iteratively reweighted regularized smoothing approach. The method performs the following high-level steps for each voltammogram:
The iterative reweighting scheme is designed to reduce the influence of outliers and heterogeneous noise while preserving the physiologically relevant peak features. The approach is intended to be robust across diverse signal shapes and noise conditions, so that a single automated pipeline can be used in real-time applications without per-trace manual tuning.
ASWIFT is distributed in two formats to support different user needs: an open-source Python package and a downloadable desktop application. The authors have provided code and supplementary materials in public repositories linked from the source. The dual distribution aims to make the toolkit accessible for integration into automated, real-time electrochemical biosensing workflows as well as for offline analysis.
The authors evaluated ASWIFT on simulated datasets designed to span a wide range of baseline behaviors, peak morphologies, noise characteristics, and concentration–response conditions. In these controlled simulations, ASWIFT produced less systematic bias and more consistent signal estimates than existing methods. The source reports that ASWIFT reduced systematic error across the simulated conditions tested, indicating improved robustness of automated peak quantification when traces present complicated baseline and noise patterns.
To assess real-world performance, the authors applied ASWIFT to experimental SWV data from two contexts reported in the source:
In these experimental datasets, ASWIFT’s outputs showed agreement with established analysis methods. The reported agreement supports the toolkit’s applicability to both bench-top electrochemical assays and continuous or implanted in vivo biosensing scenarios.
This work was posted as a preprint and has not been certified by peer review. The source provides code and supplementary figures in public repositories, and the authors declare no competing interests. Details such as exact algorithmic hyperparameters, quantitative performance metrics from the experimental datasets, or runtime benchmarks were presented in the original manuscript and supplementary materials; readers should consult those materials or the linked repositories for implementation specifics and full reproducibility information.
ASWIFT offers an automated, adaptive pipeline for SWV signal extraction that addresses common obstacles in long-duration and in vivo voltammetry, including drifting baselines, heterogeneous noise, and outliers. By combining iteratively reweighted estimation with regularized smoothing and automated parameter selection, ASWIFT aims to deliver more consistent and less biased peak estimates across simulated and experimental datasets. The toolkit is made available as open-source software and a desktop application to facilitate adoption in real-time electrochemical biosensing research and applications.