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An adaptive weighted polynomial baseline correction method for electrochemical aptamer-based sensors.
Zhijian Wen1, Yasmin Liu1, Emeka J Itumoh1
1New Zealand Institute of Public Health and Forensic Science (PHF Science), Forensic Research and Development, PO Box 50348, Porirua 5022, New Zealand. onyekachi.raymond@phfscience.nz.
The Analyst
|March 30, 2026
Summary
This study introduces an adaptive polynomial method to correct background signals in electrochemical aptamer-based biosensor data. The new method reliably estimates baselines, enabling accurate detection of analytes like cocaine and THC for point-of-care applications.
Area of Science:
- Electrochemistry
- Biosensing
- Data Analysis
Background:
- Electrochemical biosensing data analysis is complicated by background signals.
- Square Wave Voltammetry (SWV) is common for Electrochemical Aptamer-Based (E-AB) biosensors, but baseline estimation is challenging.
- Uninformative baseline features in SWV data can hinder accurate signal analysis.
Purpose of the Study:
- To develop an adaptive polynomial baseline correction method for SWV data from E-AB biosensors.
- To improve the reliability and accuracy of electrochemical biosensing data analysis.
- To enable automated detection of analytes for Point-of-Care (POC) applications.
Main Methods:
- An adaptive polynomial method was developed to automatically identify uninformative signal regions and estimate the baseline.
- The method was applied to real-world E-AB biosensor data.
- Performance was compared against existing baseline correction methods.
Main Results:
- The proposed adaptive polynomial method demonstrated more reliable baseline correction compared to other published methods.
- Baseline-corrected data was used to develop a statistical model for predicting cocaine and THC concentrations.
- A user-friendly interface was created for non-code-based signal data analysis.
Conclusions:
- The adaptive polynomial baseline correction method offers a robust solution for SWV data from E-AB biosensors.
- This approach facilitates accurate analyte quantification and supports automated data analysis workflows.
- The developed system has potential for advancing Point-of-Care (POC) diagnostic applications.
