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bioRxiv · 10.1101/2025.09.28.678418

Algorithm-Powered Analyzer for Continuous Electrochemistry: A Toolkit for Real-Time Electrochemical Data Analysis

Jiang, Y.·Chen, Y.·Cai, Y.·Zhou, K.·Ousley, H. J.·Li, J.·Soh, H. T.·Fu, K. X.
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Abstract

Real-time biosensors offer significant potential for continuous monitoring of biomolecules. However, their practical application and further development face challenges on data analysis, including poor signal-to-noise ratio when effective sensing area decreases due to signal degradation by biofouling, time-consuming and subjective process of manual or semi-automated peak identification, and inconsistencies in data interpretation, thereby complicating reproducibility and cross-comparison of biosensing results. In this study, we introduce the Algorithm-Powered Analyzer for Continuous Electrochemistry (A-PACE), an open-source toolkit providing streamlined and automated data analysis protocol optimized for real-time electrochemical data analysis. A-PACE comprises three modules: (1) Change point detection for automated identification of peak regions; (2) Baseline fitting with multiple algorithms to handle diverse electrochemical signals and baseline screening to eliminate unreasonable fits; (3) Large dataset input with user-friendly interface for data processing, exporting and visualization. To ensure optimal performance, we curated a training set of 2000 electrochemical curves from an extensive electrochemical dataset (>100,000 curves) collected over the past five years under varied electrochemical measurement conditions. These curves were analyzed using 2046 algorithm sets to identify a default algorithm set, compared to existing tools, demonstrating its capability in peak detection and baseline fitting across a broad spectrum of electrochemical data. Case studies reveal the A-PACE can analyze month-long in vitro serum data and week-long in vivo intravenous blood data, extending the operational lifespan of real-time sensors. This cross-platform compatible toolkit supports both real-time and post-processing analysis, reducing the processing time from minute level per signal by human labeling to second level by A-PACE and subjectivity associated with electrochemical signal processing. By providing this solution for continuous electrochemical data analysis, A-PACE enhances biosensors applications in medical diagnostics and continuous monitoring with high throughput analysis.

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Jiang, Y., Chen, Y., Cai, Y., Zhou, K., Ousley, H. J., Li, J., Soh, H. T., Fu, K. X.. 2025-09-30. Algorithm-Powered Analyzer for Continuous Electrochemistry: A Toolkit for Real-Time Electrochemical Data Analysis. https://doi.org/10.1101/2025.09.28.678418

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