An adaptive weighted polynomial baseline correction method for electrochemical aptamer-based sensors.

A background signal, or baseline, is typically a low frequency signal that compounds a target signal of interest and complicates the analysis of electrochemical biosensing data. Square Wave Voltammetry (SWV) has been widely used to acquire data for Electrochemical Aptamer-Based (E-AB) biosensors. However, one challenge with SWV is that the true baseline cannot be assessed directly, requiring estimation. The background signal of SWV consists of various features, such as levels, trends, and shapes. These features are usually uninformative, and if unaccounted for, could complicate the analysis of a signal of interest. Consequently, standardizing the signal by accounting for the baseline is an essential step in processing electrochemical sensing results. In this research, we present an adaptive polynomial baseline correction method for the baseline correction of SWV data from real E-AB biosensors. This method can automatically identify the uninformative regions in the signal and provide a robust mathematical equation to estimate the baseline. Employing real world sensing data, we compared our method with other published methods and showed that our method performs more reliably than others within the bounds of acceptable error. We also used the baseline-corrected E-AB biosensing data to develop a statistical model for predicting the concentration of cocaine and tetrahydrocannabinol (THC) in saliva samples and developed a user-friendly interface that enables front-end users to analyze signal data without code interaction. This work shows a potential workflow to support automated data analysis to detect specific analytes for Point-Of-Care (POC) applications.